{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>时间</th>\n",
       "      <th>GPS车速</th>\n",
       "      <th>X轴加速度</th>\n",
       "      <th>Y轴加速度</th>\n",
       "      <th>Z轴加速度</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>发动机转速</th>\n",
       "      <th>扭矩百分比</th>\n",
       "      <th>瞬时油耗</th>\n",
       "      <th>油门踏板开度</th>\n",
       "      <th>空燃比</th>\n",
       "      <th>发动机负荷百分比</th>\n",
       "      <th>进气流量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017/12/18 13:42:13.000.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.396</td>\n",
       "      <td>-0.900</td>\n",
       "      <td>119.367781</td>\n",
       "      <td>25.992420</td>\n",
       "      <td>775</td>\n",
       "      <td>18</td>\n",
       "      <td>58.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1465</td>\n",
       "      <td>22</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017/12/18 13:42:14.000.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.378</td>\n",
       "      <td>-0.882</td>\n",
       "      <td>119.367783</td>\n",
       "      <td>25.992418</td>\n",
       "      <td>775</td>\n",
       "      <td>17</td>\n",
       "      <td>60.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1465</td>\n",
       "      <td>21</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017/12/18 13:42:15.000.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.396</td>\n",
       "      <td>-0.882</td>\n",
       "      <td>119.367785</td>\n",
       "      <td>25.992416</td>\n",
       "      <td>775</td>\n",
       "      <td>17</td>\n",
       "      <td>55.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1464</td>\n",
       "      <td>22</td>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017/12/18 13:42:16.000.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.378</td>\n",
       "      <td>-0.900</td>\n",
       "      <td>119.367785</td>\n",
       "      <td>25.992416</td>\n",
       "      <td>762</td>\n",
       "      <td>17</td>\n",
       "      <td>55.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1471</td>\n",
       "      <td>21</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017/12/18 13:42:17.000.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.396</td>\n",
       "      <td>-0.882</td>\n",
       "      <td>119.367785</td>\n",
       "      <td>25.992416</td>\n",
       "      <td>762</td>\n",
       "      <td>16</td>\n",
       "      <td>56.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1471</td>\n",
       "      <td>21</td>\n",
       "      <td>2.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         时间  GPS车速  X轴加速度  Y轴加速度  Z轴加速度          经度  \\\n",
       "0  2017/12/18 13:42:13.000.    0.0    0.0 -0.396 -0.900  119.367781   \n",
       "1  2017/12/18 13:42:14.000.    0.0    0.0 -0.378 -0.882  119.367783   \n",
       "2  2017/12/18 13:42:15.000.    0.0    0.0 -0.396 -0.882  119.367785   \n",
       "3  2017/12/18 13:42:16.000.    0.0    0.0 -0.378 -0.900  119.367785   \n",
       "4  2017/12/18 13:42:17.000.    0.0    0.0 -0.396 -0.882  119.367785   \n",
       "\n",
       "          纬度  发动机转速  扭矩百分比   瞬时油耗  油门踏板开度     空燃比  发动机负荷百分比  进气流量  \n",
       "0  25.992420    775     18  58.02     0.0  0.1465        22  2.30  \n",
       "1  25.992418    775     17  60.30     0.0  0.1465        21  2.39  \n",
       "2  25.992416    775     17  55.24     0.0  0.1464        22  2.19  \n",
       "3  25.992416    762     17  55.75     0.0  0.1471        21  2.21  \n",
       "4  25.992416    762     16  56.00     0.0  0.1471        21  2.22  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = pd.read_excel(r'C:\\Users\\Rookie\\Desktop\\数模\\npmcm2019d\\npmcm2019-D\\原始数据\\文件1.xlsx')\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(185725, 14)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.0\n",
       "1         0.0\n",
       "2         0.0\n",
       "3         0.0\n",
       "4         0.0\n",
       "         ... \n",
       "185720    4.5\n",
       "185721    0.0\n",
       "185722    0.0\n",
       "185723    0.0\n",
       "185724    0.0\n",
       "Name: GPS车速, Length: 185725, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0      43400\n",
       "0.2        707\n",
       "0.1        493\n",
       "31.7       345\n",
       "28.5       331\n",
       "         ...  \n",
       "103.2        1\n",
       "107.8        1\n",
       "108.3        1\n",
       "99.0         1\n",
       "105.5        1\n",
       "Name: GPS车速, Length: 1084, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.iloc[:,1].value_counts(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.4.1'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dl3x+T+cLwNfi6s14L+YLAN6kqk8AtmkBvPGSa+71vP0JmId2Gb3Qmt8N+iaHnb77CqjiXs7Xvw/g46r6i1d8fi3zdSQbroXHXg1C/7IzhI7zUF/MNXeFROQWgL8D4JtV9emjj38KBmH8LgD/I4C/dx1jAvBlqvolAL4KwDeKyO87+vxeztcOwNcA+J8v+fhezdeLpXs5b98KYAbwvVdc8kJr/krTXwXw2wH8bgBPwKCUY7pn8wXg63BnK/+uz9cLyIYrv3bJ3z6rOXs1CP2PAnhb+f2tAD72Eq55xUlEtrBF/V5V/bvHn6vq06r6rP/8wwC2IvL6uz0uVf2Y//sJAD8Ecxkr3ZP5cvoqAD+lqh8//uBezZfTxwlx+b+fuOSae8VnXw/gDwP4T9SB32N6EWv+ipKqflxVF1XtAP76Fc+7V/O1AfAfAfiBq6652/N1hWy4Fh57NQj9nwDwDhH5bW4lfi2A9x1d8z4Af9yzUn4PgKfoRt0tcszwbwD4kKp+1xXXfK5fBxH5Uth6fOouj+sREXmUP8MCgT97dNm1z1ehKy2wezFfhd4H4Ov9568H8PcvuebF8OIrSiLylQD+LICvUdXnr7jmxaz5Kz2uGgP6I1c879rny+kPAPh5Vf3oZR/e7fm6g2y4Hh67G9Hp6/4Plm3yr2BR7W/1v/1JAH/SfxYAf8U//5cA3nkNY/q9MLfrZwB8wP/76qNxfROAD8Ii8D8G4N+7hnF9vj/vp/3Z98V8+XNvwoT44+Vv1z5fMKXzBIADzLL6BgCfA+CfAPhF//d1fu3nAfjhO/HiXR7Xh2EYL3nsrx2P66o1v8vj+p+cd34GJpTefD/Ml//9b5KnyrXXOV9XyYZr4bG1DcNKK6200kNELwjvXFHgcGURgYh8ixcN/IKI/B/L3/9tL3b4sFjhz2UBiZVWWmmlle4ivRhM/28C+Mqjv70HwD9R1XfA3JD3AICIfCEMY/oi/87/XUQm/85fBfBuAO/w/47vudJKK6200l2mFxT6+tkVqrwLwPer6rmq/goMb/xSD+o8pqr/XA1P+lu4vPBgpZVWWmmlu0ibl/i9oYhARFhE8BZYgI3EwgEGUo7/fil5McS7AUC2u3979/o3Qjz0oASF+K8iP6sqTAG0TGAVHa+9MrNVLnlGvWe9H8fD/+q9/XdZEKo1ruU9ern10T043mE8d6C4vr7DHe4nmv/e8TnNP+c74ehn/13K3NT71Hm6sIZH4+ffr7pXve7C/JexHN/jsuuk48J732l8/K4KgI0C7WiQi9haKy7w2sBPenSvS97tRf0ugG7U1qc+qAMyi71f4a94frnfBd49HvMl/H88pgv3LjzI71zGL8fvHc88vu0lfFF/l3607/n5kSyQ47ngOOToXcv6De9xfP/j8R+/I/KZ2i7hgfrdet+jcVRev2wcl37f/3b+xEc/qZeckftShf5VdFXhwGdVUKCq74UfIHD6lrfpb/mT/yXanIupYhPKTYajySIT6OSMMSO+rxOCCcgw2nyCG6AbQJtCp5xEWYDpTDCdA8vO7qGTXWeMZw8fmJPCxzefcqYFaHug74A+aSxSmwVtD7QF6JO/QwP6xm7YtwhGaAeBzHbvvgV0sofJLGgHu6ZvgL5TnysTAm0PtIONQSd7zqB4Whm/v89yqjZ2n7M2u2A5fkd+ZypjqqvO78I+U1cmzYWluhDTxrURaMuB6KQXFE872PvWNVXOXRGw2vyZEy6Oye/BeanrGL83e6e+Uywniv74jOl0QWsdqoJlPwHPbHDyycnuQ/5aci2Wk+RdyPiefVK0RXyObCx947w45Vil+5g6sNxU7N8wo92cMU22iMvc0J/bYvepKXcX59j5JfaQz78swHQwXqhzZnxH3rR/h7UtPNu39qh2KHPLveD81afcjwDQt8abbZHYs8HHi6DNNjZ7psYaX1AYXWKeZeb+BToVInL/ypJrieZzuvj7bhFSlnJhMBAFwY/SJb7L9aVx1A7+vHPu8VRO0LKuVXlzPyDXiGvGuQ1ZM5scqgpBKSc4Vth4fuG/+S9/FZfQSxX6HxeRN7uVX4sIrioc+CjGniAvvqBAfQK3I1OEwMcodOw6/7v6Qm0E3Zk2LIAF6JcokbYHdBIsvhDi35tvKpYbLnAX+76opBWhQPN/4/nOUMGwKIt6DjSRokCAfgLowQQ/uvPaAkAE2zP7ue98gf2ebQ9ok2A63fh7wRgEU76bTsDhtMwZAKUgrVaeJkNPZwLd+sZreR/tRf76+4ZwDYFic0Wh0XcIIcwv90nRIGjngJxLCmkBGudX7R37pmy0bT6bm5mKlQq9KiLtYvWq1YLrRaD5mPou3zXmhIrR3ymYq+68Zkqh7+z66VwgFK4+pn4kwJqvdeQ0ND4/54fPpvCze3HyJN/xyPyczk0gU4BTYEsvwtmVyHLiv25yz4Rg0/y+FiPF9oViOZV4P92k8KHQ7hP/nt+N+RfjozZzSl2A++9UtJyvyY0dW2sNBQoRKIB+ynubgNSOEJTkG+VYuaSu7Gw/+LvMsLUrClLq2mNUCG1OJalVnsy+LS/xmPj+4oZHLwq+LQjjYQrDVNJQdYMt7gWfY8pHveyhSS9V6LOI4DsxFhG8D8D3ich3wXJL3wHgX6jqIiLPeKHPjwP447Ay+hcmZ0qd1CdS0n3uOflhxftEhra3T6EnufjHMIhOxZpd0mdqM9D2ZgmGMnGB2HwzUrho2eCtWFWd9y1jbotd14vAQlP0BjQIOhmtp8WwnCAtERf2YbEfK8Re3q+ZAGvignUCUvAWoQUAPecn3lUBHIBpkRQ8KhAB5JDPh6gLeYl3j7UpXpOKCaQQfgKoKvqJpDXrY++TM3LL+7W9mFKabW50c6Tol4vPrF5dsJUr1suMhXYYPTeus3ag7y7DCQB6HnJAWOTHXoe4MqDAn9xKpqdzDL/UMZgQkLDslvIuFPjqSpI8Ww2KwWInrxYvAi5AeyhkTe8UxldtLym4/Z59Y+tI4UWWUlrSFLAtvQkqHuky7Nu+VeiO+0diLXQhHyt0K/GsCxDmYsrA1ktyH07AciPXCcBgXQ+GWgewc/7w+ea8UPDLXHi7CGsVYDmxD5YbwOZ5MSOgrKUUg8qMpMqrNnn8O9cllc6432J8VNRUcBVZuIReUOiLyN+GtbR9vYh8FMB/DRP2Pygi3wDgXwP4jwFAVT8oIj8I4OdgdtU3qqpvQ/wXsEygG7CmUFc1hhqpWFuySFjeoLVZNm7VhBBA9lImV40hnNEAmyi6eW0vmPYUwpJQjwDLKWKVdQI6Eqaw57rFRQsICctIN0HA8YXlxQWncL9dNmoD1IWQCXu/15zvE0KFjDSn1VdxewoZ3QLLDQ1LLGINXQ3ioBUsKWh0a8YkN+l0BmPKKZkWtBwlLcMKn8li7xAJurT4kRtXyrir0EYHRApWXhQsfF0Ix8kyzt8AL3RuTtsobW+QhraEHVCVoL9Dc+XCuZlv1rWuE2V/2zyXvEghRyUWnhWtwSnXVycqzDJ2t4ArZtu3qRwIlV0gMUHMcYbxE96shBUaXiiV4yJoS5lD1YQum8Z+oPLd3AZUxNZh0phfIR9tAbjV2vZFmBXYRUDIw6HIxa4d4LEJ0EnCuEk4b7zO+DH3Qd+lIdTOc18RVk2PSwLek8UVsSYvtr3PpXsy/VTj2VxTwN4h4FfCfOx/ioIE0HMqBomts8QeHGCxjSudbVlnKnrXw/RC6rivohcU+qr6dVd89BVXXP8dAL7jkr+/H8C/9ULPOyZRYPu0LTrgQpeCndcUC1EU6L5p8wK4BjXGpMbe3M6NQQEQwonWGQAs5flNobu8ucyu+ZfCVGFtOENW19c3tBQXrvl3TQCUZ1MQu3uHlopO6/gICcGtqea3mNzyoKW8jBYKvx+KklaI36dv0trrW9+MRehS4G9u5zXhppb3BNxl3duzlp7zaXPqc7mY9SuzvcNyUjYU11tyE4QF5GvQZgCM3bjSDqiGj+sKNIl768YFVbEeGa8BANmIQW4VEy9U2exwK5Vp3dA1YB7QxGRKqx3ErNKDxU8IWwmtXc37VZ5aTjUWU0QHeKeHIjHlQMUfgU9JfrT3RxhUhAwJqfElRWVQWBCLb5G/MwbgCng/4swBk0mBJijkZzekplRWfQMI4UR/pvRUCOSJUKC8ZYkD8HfCdhHDmE1S9q1i2ZkxFJ+FMnNe4/siv09YN96BMYAFCT1zfPQg5pwH2ReFtvW9dpJxxDabEShq+0EPEoqCni/UjbW9P8s/X04dhi3K5phe6UDuK09lciPAidFV7ScjnkWLuAZ6zfpwbTjn/ULLOqPp1je8WyzdGTGwbHoRvikvCMFzoJ1LCJxlp2iTpIDpxnRhRSugxZIV4rzENXd2XVjiFAJV6TXfT7QOfSMuJw4X0cUsVgTvH1ZdcccDb57tu5yPvtVEhLph1n1n/4VlQova14yWFJZUahEkKx4aBdFhl3+vLnfCYLC1CQsy+WM5SQVOgUsLk7yx7BTzTc35pqfi8woklBKud/Uq70DTWQpAKfMZ+sbjBcY/EtAbhc+At9OjKsYMDRNs3WOrsE6hdjAelCXxYfL8fAMR7BQF5Fw8gO5C5xQhjGOOPEGgOZ6+uEGArStKrjdSUdlapOKspC0tbVkE0xmwPQeWnVhcxNexzZJ7S+x7fYPByq4JHQGrubdQBXcNXiccZAI+Aq8OFVKZ8f5h5e/GgCuhnoSYJALjULuPvTBCcav/LiXOp5PP8T55sloTAkQ8qkHQRUNpAe6ldFj8bjZvbdrjSrr/hT4QQZkG33x058TxrdkE6YU0PE4cMUsgFAWFHASB1zEgI3CG6R48ckaRc5iF6kyw7DSi8+hAW8S8S2f2dpAIuBJrRLOgMJkGINyjgUMuzZgiICZXPMuuuI9LvktANtwAnk0QwUjCBO5V9I1ZNxVeogVVg1eRbaESCgtqY6yeQd8S64dnHaSFyA3bJ9sMrbQEs+/lNSG7Wt67ZlXBoRqBPc8Cff63LmGhkmjxbM4SxmmLQI+EN2EL4qI1wF/HWuMNDORK4GvA4XG1MdDao4VZeI8Wdfc4kkJDSHMsNCCmfSoBLQaPNoE+coWlT2PHv9PpdWwRlnrMrRsJfaowKEaL87wIVR2t9ICSjqxewL/X0zugoUKBDxT+3wE6c2zFYMPF79gXNRVegThBWIMKYEp4JCBRzWsa8e8GzI/4rT0mwyw6rsmwx0r2Tp0LojNg9hWhJBjfAUdj7TkvvE8kj/CeMB5unjwCBVqXUMzhvYvLJ9/PS4klHNP9L/TF3OYqBPo2J6cyecUoR6bxfcnJg22m5QThToObwLG9wPWcgXrBt8lAbRbIHslkTiqCfqKBKVY8tbpjy6kpDcpGLIqOI3zdmYZwAVzBhPt29Gzo+HelVeY36ZHe5pkQkz2j73SwggCE1WYuaf5swdi4pWHpXTJzocJIhXkBw8UH4cKxUXDPZT2R41OB4fv0RBaJjRMxH/7MNfRnzFOOWwtvVOPBsjXcE2ywxlZCXqrB8avNfROkR8HwuibIeWmz2DhpAW7tHep7B3+SzyuGXAQtYIJfRENRbZ+zfxf3xAJydMUDAZbgC+dlesauWFUys6cGTQML53dK/IFzTu9Sm8Z3h5Rgf/R0nvspY3f5bjWrBsCwzt0Fvgl+8zw4VoPNRt5MA4SehmZWzOKGy5QeHtO8ZQGmxfmjCGamVNfYoIjg8Ii9pArGdFlXfoS0RIFly32q4flwn7U9An5a3BCg8gDcWClxltjzuDPd90JfG3B4RDOzIrBhydTEuumJL04I1yssYBcEosDsbiw31BAxxygYeg2guJBrx1lAbfzv2Bqnq04rZpnSBa/KSum2bSQYB7AgULstqYB23Cy+mYuwYTA5YBERy8WuisYtPhOyJeuCAbVObBGRRhiYImj5phCOGAVyDmzg9k/fGW55PFbMZQ58E8mESN1rezgUgvQ8gNFa7/n34AeFoXkF86UHppNkHIN84Xnj9KTaec5J8AklIZAaJH4GNs+mUEBACgUSoPIoHk33GNOyrXyc81qzf1TH97yKVIqlR37dpydRU1INnkmhM4x1Ka/o1yRWxfkUs6iLMMOBzzHeI89XhR5xNBpwJc4VcbDCR8GfFcPnXHMPlEQH4uHovqVpBBT+jkyzhogBoBnPtdkVZslMi9hEUWCAJUtUuHVIT9W8RyyO2P4Phe1rTi/bICw+v7wblYPkHgpDx+clsuvuwCf3vdCHegYK0t3sZcKq26nl72HhuwsUOGYHUDIyIqBF64XG+UIBnEzESQbyeXFPGfFsFjE1t8o7YNDODYwM4L8yy0RgQokCJuTDTKGbG8CERFZf1vgEszAs3VMxP5rvZcG6kvmwlDiCyJAlspyk1dG9MA1ABB8r1s65Oia+XxU8KMwaQnouGPSRcEADIg/M32M6s2BoLWQiLHGhGlUz1pIButwZDOTSIwwMvbj1gwlFN7DAO/MjagKA1mctulETOIzjyGx48gTx7KZS6MfncC417wEqq+pBFbiDazNkylSl6M9uzrMymwLVya1hLXCNf7fNNvdKK5iYPHLt0M2gWIAQOplZZtfUPclhR1aNkL9osSTPVOVDnghIdbbnxV53WIf7ejnNvSYKyEFir1RhH0rOPaz5RqYUH8d+xPd6GjRjseFU4il8176R5GWx8S0n1dBw/tsXZUjvrCjMrhKpttVTrllwfI+r6L4X+qKIaLq60IxqO7o7BWtddlnt2A4SzEaBLIvlmI84KzKYWBamWngKQGkNAIPAJvPTvQ/LbAssmyLMblNISdmwSMvD76G0crhpQeveFcqemm2cq3hPD7wmJiuYbxYMfzb3X8sY6NGwcjQqDqnQmJXk70ahyApg7fa+1c3kmCpDxpwXb8SUU9k4swmmcMEjfjBWS9Z7qyKCkbO7zKzsbOcZywhemWCQlRzfz2o6qkUripK/Xta/Wvp8j62GQu0YvTWr75DguaogG4Ol+5KKKsZYsYF9LQbYSO356v+Bc0plVesGKoyCvA8t/xhPB6SbR2iL5NlRB5gV3yQTHlA8X5EoxOocv6/ndCbpCVYHqdSxTGfGjBHLcuEnXpRXvWaVsYBsczuD1HCFMz9SCwV9XQrUYnIis/qgUhIDSiC4CNnEYhEeKkRib3QoJsZtPKBv8Y0iV2B7cFOhGmcnZZaRGn9GdXZBIsKoEUA3Wbkegeoplc1ldN8LfW0miKYzGMMeTCDQKrCcZGS/EU6Ya+ft8zYBh1uuMLYK2SAyTmoxEd1xAAlBlHFYRgUFnxqmOefik1GZBjedp8WlmhuxFhuRiZZdeW6xwFGYZTrj/Ua8EEAEfGuONZC4/XRuWRJkoPkmMjOpp+IZct3delL43HNOikIIQeHf57W8HzNaKkwAdauuWlHnkpatzxHno8JHzXHwZZdek8wShXQ14wfIjdT5Nxd4HNeyBfREM5OrHwmnVjBgQglHypbvz1oLen12k8KPzKUucCMUw/OWGzhKzStj4RjnnJ8LmD7MgpzOvW7BLehYE7eQ6b2pe5SQYuHT40ARJp4wESENV3LqRXYLENks00EGIcfWEpE1V9oToCcv8Nks3OM8LcWDbIvxynSUnUPLmwF5wGWCK02dbI9NR5k/FkyVYU3Il1qgJ6AIf451ye+0gyv45p71DaRhekgoJkjH36vjCKQiJdRT9wo9foAywq1/wlCCcS8d0X0v9AGb+MMttyqW1O5yLHRbYpScwMMjKYj5d2aX1EkPi1WKFU53qed/y05SKAJhCUhHFHfxBrREw6IihLRHBjKnEq84WBEJ8cQoKOJnvonbIkc9gIzR2QsFQLiES4FkhjoAjn2jUC1CCojN3yUDeVKYNwRRCZLbF1xptNwwlB2MtQR8cp5j4DMr6qXuolMoMoYSeeyEHVri84O1WtfQlQarm0NIuHUvs1ghVnlGpFYuEkFBANi/xpX+MbwDOHxj/BNC28eg4nGZQz4jgnAuyCveTkOGLQpCAdHbvEzx8HEboKsOQioFOic85yu8jrDMJb2S8uywNvn84hVH3yfCnw6jMTYDwFOr60DLj0yT7omRs+huKjnnAZt0hFClYmt7g2UiWO0vWgvsqJDD4Cr8S9ixMRbIuBLrcJzHoj0E+XMWbJ8FIBK8Va16IBV/HUNde4Axh+Q9xlzCy6n3LcgAk1O0SUCs0bfqEnoghP6FYCWQKU9ANFDqk5qwcSa3nHdXDPsUmlQAilQcQC5WBNG4AHQRe46BcMAyjWMbNHpZdLrtxxkY3CxhzTIfd7Hx900uJP/lM7mBL7h3PifqbmBtOBcWq2+M2k8lGIxutwchA/Yi7syAmW/MSBc8ilWo53JHszV/x+zBoyFQgLzvBXLlEQHSnoo23HV6Oi03PN+XCjvSMI9ddD6j7Aa2YggLkHxWwWUAEkCq84ALjKZSFJCvz+Qsy6+4xR5N8EJYuWcxKZaWQliBXI9BeeswrnaOVFQUMCVjhYKquZHSCyxHwcv5Cy+lGFd9YwKc6zqd5RAYUxEfY9+ZIA4eK32z6AHY3EqsVZuB9jywnEhCGgUfD0iuj6+/nGrAjhGv8fGrutdLg8LX9rh5YDtY+uZQ1EeDoGFQyNVzZI1IKPqeiiKUOj32Q/GoBRefs01Z1LeaXj+QSAD3eUnNFBZKSvLvZXTfC33pwPZpc/0XF+4xScTY3E1sbKoFgBaiaC5IZuRImcR81tCJ04WRdIHClQkAmRWtZ8BvqrjclL03In3PN/RymtcEBOGWRN1YAPHz0apqLrjNGpDA5gfvpFoWDLLStWVQqmQl2fsXy89jCOIZUoCMDcgavNFcCp+hF8mRQIRIuO7M5+4eILGUWQkL3hSxZgM4WlqLBLQRUB4kcU1uxBnY1IraCam0mr/gHgO0FvDBCRkAISSGed8jBXhToCmhXCg0xsvmcYm9p7ALQ8KXKpRd9SY5t3TRuU60qjeW08/ahLCUL8x7fvcC/l++spyWTJCS+XKM8Ud8xLvAsvUFPZ6+K4qCxkmB2ULhH8GCzCIj9s7AbafnSSGIouzobfjcDfG9gwxCP4ybedzvQ4yjubL1MS2Oh28OEgVmYcvQ49cUuqhjcu0zZCCVhAq+x3KqQ3fZqijaAdg8R6MpZV0kXNCTpody8Ewt58HlxN55vokr6b4X+sQ528Fzw1u6iNUNJ9nmssVfbmjco7mrHEapWw8UEoyWHzP6tDdrm103yfBMdbSLc6xsexzR9o3vtynvH5ksbi2Dm3Ma36PCJ+GFUFGUOUqrGxcCn/Qcwipa/Dl+34gJNBNiGRz0m9Ma8u+G9cEA2AYAqyRLlgEzquRgcpL9jDhf8w3NDefzOOSKUxjSiFWgQcI7CGXB+RfX/VxHF2ZsTFete+K22mCVkUUR8NkGUXnXzBvp3dEaHhg0BLekRQdXiNX5IZ/Ed+1vtWI6BIoLyFYMAWyySI4N/y4lvwc9qlCeDifac0vBEpvyeVl/Y5sCpl1OGsI99CHnfEojYTq3ZxPGCT6kVcqU4WjZgOgo26rl7J/pBCzbAn8yU6co0zBWJqA3LWuRsgBCLy2FMZ/Vt4gWCFBrwxC9spZcw4jVNe5nHfYV1x8w6Cc8eIxCmns/+JcyhfwnueZRx1GgtZkV8rEItpY1m8iaET7Alj6QVoGUCUgBU9xwCkdaGT7xDNpGtkfnYvsDiuYPDerMNbM4Bq54zuSCwqjZQb0huvRNLtgzYi+WTtY0WsZyPHVzHAeR7Y/5rGFuJg2OsSCkGKzDry3JbMTVDYNFZPTYu7lg8/mmxS8LMPUU2IL0iALCKJhMtWDDcpSyJgzwoVhgbjb3SYMjw7MIL0gS5/Y88GWnSNw9eUJ9XBX+oRIKSyl4xP4WacBUBguiDQDHG0rrmCikNgbHsD1HFS5UqMfWbo3LUNhTiNXgJb0OxJkEvo5S5t9/Dj4pz4ICmM2ACaVS5oWKr5/Yf9H3iUq5jN0UggT02b0txOEWAk9nFTkAE14+zOlcMN0+mr4CVwDuWW2A3gEpCja8kKJMZJah0R09+yhIlFRow15w78mqnm2d2WaCyQKcZxpTtXkin0XlZJ6F7UUaDBAJY5XzzDWNQLaWd1mOPiMcSXLjC32srQjZcUjeO3b+Kj0QQr9vFTI5TsVAS7XcmvfL4MsyTc41PFM36YkzF5wbupdsHlqb4kFCwIWlu3osMOru7nLjMsWquo60girkHZapWrYHy9zDLWZLACmbDCjM5a8cmzLN14CcapaFJsMRamFGy7SX0g3UxhWWlVuIxIa5wS2lNDkqYB2uC3zDFvyzHWD5x4UR2yLeXz7H2oh9IC2fIdjlazW7p8EDOKL/iY9581zZ/KXVQyhYIKCh7o3X6DpDEB1Owxug8ji28mMC7HvTWXpvprSMSaui0wZgkx4R/2aZaAXz5lxOlvY7KBwB5lONuZL6b5mHCPYRWprKPABDBsh0NhpFfI664g6FyBqLJaFNKncaVowPhQFQlCmVW+Vv3TjfuXcmJ6lcyMMAeUksbTQ8b6tWpYcNmNKItiJTGjrE8rE9urcrbN363C+ZSMHMLfYIqoblMFf0QKAR+9PmgeVSSMX5R8vYXFtMmUSaLesaiuKJsXYbf6ASmvMSMYqesZLL6P4X+sEcuahhQRY3sbqWihIQFBfUboGI2iZayFjISWVVXCfW6ZYEK1MtLQtRvEQhG0VDZKxiGaAjCr7aHpAt0HdpQfYtbBELrie8V/P94nn+C9I6CAvSA3Fx2AQKEzBYxLzpIsyns7JhiY0QP1RYS1y3KpcTZ/ySHTQUDlGx8DQwn8twoYlncs6WjDHUSkhSBuLH54UALi53VYYM3uM017bmd7fqCQqtMnW83BX/UjJmUDaqW878QGECMeAdCj7CbwKLvXipv9Ue+BKzZxAtvdTbNp4uo5fjCimw8A0iYyPmTDmeFBpAGjdDYR0zaohpT2alB/QDjJhzEZCMLTFe0NmXpsVXAy4y+Eayw2TxaHkfph8y3bYaObw+qt+dR/sG0EWsuKlRWGrUQHCtwmMtPx/HNQgVVa+QGHnbO6Ton7PTarYvGWMP4sbPdG51MPMNz/bbFv4kb0h2+2SgulaZU9EedwOIlFR6BlrlYRb4Edq+jO57oS89LajoIVIVwVG2DIVETJYXlkQnQSAmZgiU+qayBm4SGHYweOPG8BsAo7tHrUyrax7HppPFJljuzwGra+9jBj/uud/9/WpqW81maLMAl3TpM7dPrOGWjz26kFIQCZ+XjKIT0EWjNS8th+UEpU+5HRQhPa3GCHIvOYbArCUFTm5WH+Nc3NLab6ghLEvjgaNWyb5+DPjqztai9ounAGJv987NpB5QO895G7wRjkfyXe4UyI15L245vcy+BXSXXUqr0hQ2qaPL7t5kO4jjx6lQjnsTBS8K0uLv3j2yI7yVyFKhxb1LIQGkQiUUEvNfsrnYarvPrpQID5X0VuvJJJdYwj7HMe708rbPen8tHwcweo3ztvDOkvdqB7EeSSoXzh7IgK4roarwitxgMaUpRo34HxQBsRHCXFSGPQcgDDL1OaKCPcAyaeqpWtVIPc5Sq9llzBrMFOeE5GyLSiiKgAmbQW660ai/uYrue6EfUIcLkmmWLIKaFBBJC6fnv5G5QsGhAoEOm5rashVmms7T+qguuTU3Ki5ot+f3G3xmyWCg0JrEsX1EjxdCGMPGBy2FFJ4M6BxnX0yLpEJoGJUNsxr8faqVFlawptvLdyO2qQcxTJeVt4Wxpdyz7SXiIcvp6AKzbwmF6OIWLTHIxWMerUAD0XcEiVtbcF0x+/dZfBXzXKABnpEQPFAFjQe9mfFBhdFmU9jclKEYioCv2D+AsCwBRDEUYAVAXayN9rRP707L8wL/pbfiR0PG2i+wPuu+Wa1Rmg7PJx/W+bpA7j0dHilptWX/VOHfZp49nEkONZ1SJ1jjQMaBuvFW7YIKeNCQQpnxslpvwHNwyxgTulAcbvlYyjjZ0js8YLXYUnyXzy91NQzwhwftvDu5EI+/V6+x8Eo7WCfbyFZzfmznEnBkdEEFIv6yMC35gAywi1cIl3267Eqt0JJrjZAbiH0ui2RLmGIYBj/518Tfmd5Ub/Qu74KlLyJfAOAHyp8+H8C3AXgNgP8MwJP+9z+nqj/s3/kWAN8AY50/par/6IUflPm3x4EUoMAJzlTCtgyakxQHT7PQw5m6MlBYQGQg19g0zfh8anVgtBj4HbPISnVwiR/EfTewKtCymbRY00PQ1gUXoZdOQVQseiq2zkpdZCZNQD9SXHZaFJN9xkBezYeumSfLDcXsG7IdJPBfct7ksJdOgG4tw4BxlTh56QgbjcIWMr5bw8tOQ6gwm4QB6PAMCKF0WGGZ5lirh1EDhF0wKBbmTzPOcYyBRmaTp3kuERsaN1P9NU6zoudUevuYsi2WYhFmNGIuZJcdsgIbQASx28I4Ba38Kh0Apncq+ZYKx72B8Egm+MuTHxjbKZlWMX7yskTwuzn2HUpz8Jwl4jyEXlH22nxKpeoKnhk5HZADGwRmQL4GvUeMH1HnwnmvwtzSqIELXjKvqV4cYwYBG7rxtnGRQkOQMcMJUZVf421UOIQuWVgZbReUxk6wBoiwDkYehzWpebAoSlQQ1dAZHynrfQd6yUJfVX8BwO8GABGZAPw6gB8C8H8C8N+p6n9brxeRLwTwtQC+CHZ+7j8Wkd9RjlO8nNzdQbEeMSNa4Q4GjwtGnRA9R6TZTZhWxvRM5s3XbIoBGqkzI5rZHZsURjXtqh7lVptqiQdQAwuMAjCBLM4UxKirBS/Z2Q9FYAHIdykQiQ8zs1vckzBhJhFAi5RCuus0lIoC0S0A+MlLtK7Frl9OFDpJWNrhRnvQK+MgOeCBUd0idK88xg0QYiuZQmVTmHWcwjoEgW+YTuiqpcIdMhvUrDCVtPY4l7R8SRErKOOb9rCsqMeP2PPoe9IxBADj/YshEgpSck1qjUjN5qn4OCY/wISemq+dVF0kjkWfj0U8gf3yGaUiNNox+IDTk871Oi4+1A1wcMEdsTFBCKdYV5/vMHaouHuOBTAIN9p1Fy+0n5T4AL298PAcBtm51zj7OhVPgvszzpBdcs3DG3BLmgePJHwoaYhQ6FaDKITtyBNpvKU3Gz2BeG/hOEc+Ca+L/NzFmsSV1OpQOpoV/OQt6/dzB08Qrxy88xUAfklVf1Xkyqe9C8D3q+o5gF8RkQ8D+FIA//yFbm64mUSwMISi50UTrmCRBRuK9Y0tiKVwucAowWDABHW0GqBr5ZNv90rrLZpJuYbtzC32z6Nfif/MikMqjHAtxR/ml1NwMdiXrYw5SMS/bZaIS4S1Hj10PM20eBDSJYSxarYbaGdAm2TAGYeMBt8oFLI8HKSmisnG5ocuay3CCTzXmbuXthLMn+dmDk+DYysMXjMY+N1eNpsssE6NlH5IhRcZQBQ+bjUTt60e2pCxUqC94AeHOpCPKQxq/0Sfock3XrUMXQGzNUY7RxxrqE1CKEfaqXuaFLzk1YAFNPniyPkIj0t97wQuz8+TTc0SZjxmn++RFqQMsMhyUpQYRoVdrXB6jW1PbxOD8A7LX0xwLzcBtAzmBkyoUhRP8r2tm/0SXoqftzCdIRMcjvhQt4omJkSjGngHr8ko8SjO3WwWes2DJ3GMlCs1HVg6gL0d4LN43YLtgdKAj+vtkCD3YVuQje3o2dNgcqMKdf7pzTKgLsmTl9ErJfS/FsDfLr9/k4j8cQDvB/BnVPXTAN4C4MfKNR/1v10gEXk3gHcDwOax10ZgaWhzyw0+Ycx5d63dd+JFCjkxfdJ0QY8s5xqMa+cSi1WzcezamhlUhLPSGsogMAOysTldcEXEni0gKPwDHjKIpgoCKrNoxXCCOD3KrPW0zKdS4i3q8AuHrPkv+7MPljWFX4cdKOHv3nelr0/tyeIKjLGXDGhKjJlwRAj8c1eGXisx9AwvmSehiELIM320fI5SAu/zLmUuOw+4KJBAFLxQkV/SmTWwVQqZDQqDHZH/ab6pUcdB4T2d23sxfZTj5DuEYtr6WJF/J04+7SUUAZ8Xh5vwT1yAInTrBWHkUCEysHlIJctn1PTVxYX1oDw3ecobIaO02NPAWk4ylZpCrbPgLTww56XmWWJqvG+H7ZSGgBR8yHWeijepGxOEfQP0W/m+KTMksO/BYGLcpGfvLFrjOhVlJjHUWBdeE0K3tCTntVwndtlVKcaA82AtduTShSGipRgN8PYz4kiHbxHntYDcyn64jF620BeRHYCvAfAt/qe/CuDP+1T8eQB/GcCfwOW655IdBKjqewG8FwBO3/I27VvD5LfPpbUQriLdnAOGgIg2QE/U+90kJh+pWdXVnWg12Pf7DjgQzglLRgJHp3aP7y+C6P3DGVUTUkBaDu0AtNvFGuY9/F2uDIguyXiBz3Y/PHlGViWLY/RbRLyh9haJeWn2/bAW5oK99rTMkrld6TKLwqGKCICLpLWOvC9TAm08riwEYcXSSkf3s1Hdg6qVuZFRw3mCC4NymhXnKHqQzwlFBNu5wDLhZC0fFgbhlxxf34yZHrXR3THkF7CKj2/yVs0VPuLZt8PzqfT3wMYDmLSEGXuZPJZBWITl9eTbeHcOh2CuuOe000FBslFh36WHyZiKOjZOC78d1KrQb1BZJ08bPi/54ArFbM1rphdV+8uIOI95wWJkpnQThDFniosKmC9axn0MV6Gb4qTQDriv4uiE8ihDu1vZ5HMfTwRMkfJGHFKusYOFQdniTVk8A1kbQKue3q8CPPYQCsPlG8BW0fU+wWeLXID/4Io04DbfZ/Mm98dV9EpY+l8F4KdU9eMAwH8BQET+OoB/4L9+FMDbyvfeCuBjL+YB7SARDAr8vOVm5wICcDyan40FGpmTa+5dZBwsyH42ZI6ZL2H/9K0tUJ3MtiAbaCkgIlAG1zxgGffQVBgsGAqs1gVznH1ZGC/azgqy13rJpxbNDT1ig3mfKpC1ISoegykbsLTCqAVSyHHY3CmfuRdsnkdkHEWfkFj84sb2bO7VFguK9g0G7mv74kFRQLqQj/Xg33cpYCgcBsG99cMzOO/F+idxrSGmADmHUb5Ob2GjWFrZjP5uliKpaA1ZFKXleSW4F0K6KqcT9crX0iqDZz0Qm+WalrUgvy83nX99LKjD45xQ+QJhsBCii3u68JzOvV+LAPONtL4r/sw5rP3coclL9Sg/lPmOeS6eSmLa/h70JFCO4mwjXw0B2k1eT6go6gHCyPHK4NKOJOJAxduhwqhkLaIxeOLTHpBZoRvBfJp7M3hL4nY5/mFPIPYllWRtwSAHHyuVgiAhHufFIYmE81H3ageEwZ4r6JUQ+l+HAu2IyJtV9Qn/9Y8A+Fn/+X0Avk9EvgsWyH0HgH/xgnd3IRTYFf+TUm5O8hkZrAzibR3QreHbfXMEz/XEM8MtLM9M/B+DJbhoaa5E5THJYJlys2VEP68dNkZh5mWnkFBeae3oxt9LM1BD17keoB338/FPB+9JzmASC5SoGOjii6efTXlvWhIs5mIgHDAhEa5lCSybMvPTmYp3VPHHwSpihSGzcHq+F8Td/tNcq+NkFUJQNZUxNkNHeHphsZdr4ni5Gdg8j+C3EBCeQRPFR/796F9fIJ/Og0WozIuwq/jt9lkEVj4o2CWvHZCk4iUELFSEzHAwumis2bF1S5IyNt5r/xgSi3YhWgPNIojx9jJWzvHm3Ly2+REThn1X2hb4Xohzet3Czg6b5BsNz4YeVlRJc67Ke6Pu8yp0t4zbILxmbRj646cniJTSUtbABfPCg1kEOHREjCEn05VP8XIDipOURfWc4k6lw88X+BkBCOh2iP2V+GKr+6MozxiO77m7Bu+IyE0AfxDAf17+/BdF5Hf7kD7Cz1T1gyLygwB+DmZnf+MLZu4AwbhLibS3A7ITYdkQgXvv8qW1wTIKyJz7nKGBgcgwhCSqFcq+LgeJU4FsLHaQuRT3VxZTMDVXmXj/xEg7EC0IIkrP9M+Dl7e7cA7Ip5vWN0sxccjm6ZzLJhVExWWZTVRx7Zr1AiCyQmI+ivsbQa2enzONEkhBVDH1oQe8r80yFYbcIoLLHTYvvUsITYEABzuMGgpsbmdtAhVVP8mfmZHDw2vaInYqVqVirVIQUbgQ7qsCdZgL37hx7uwdiHn6ETy85D6b5zNe1KuCLRu2fhZN7GgoeFAwvohUQhCb3wVHfeWLJ8J1I2Ye/L0UAVKMHSAFX+SLV34RgxVwg3/L4qDYl4xTOV9wjgkrLooIfofSqVl7zodDKqwCaILIPIvTr0rKdPUOFJmcMOV+CX6d3JNmpo97YcKUUOcN0IMLONbmWRsgLWssOF+6kZABKiYfajdg8TVll4BI8WbyCXljWDtEDCBigd2gNZR3voxeltBX1ecBfM7R3/7YHa7/DgDf8dk8QxaLgAMA+6UstXUrrRoy0pabhlaFWxsRNMkNFFh5Q2b+KAI3puvVt8TXJQ94kLIQxSqi1UBBFuN09zIOWwZyw/IAChc8ZOwM5pTrm2DzbLru7BtkrRY0GbwBOklWFHcYE7VkorAGpGwmZ87weEqqGjOiqnVa8dUBZ6UrWiygCrFU3J6b31pHe/HdNtvcArneEVgrgsB62cgwjsx6yI0Rypf4raf+dW/bMFjW/I5v3HDZ3coP/qyS0XEMWnpV4Fu8xf6bH0Faoq5wWOQWwU2fyNq+gfGCqEq+ghQWvGe2UFWMtfguFLQUJVIFDA2nqRgwAj8H14dUcPEq8GPMvJYycGPXjJW2WexIqjUs7SDYPJvGF62VIVsFiGNEjw0SbFNxTD0DqTSo2IGzBXZeDAY3vi4YNQdANHmOwVPGL5JX/N6lUwDPtYWW2FMkjIzPiqaRxQMPSNgVqwCZEVXf+wq67ytyGZhiVHo5zWAp3WUFhv46TJGrgb5w2Yg/E3pxIRVY9pTlz9O5hKIBEDu/7REbeUgVLRANPGc4LLON1wZUK0jsfzXbhIKqnoI1BI2bYYraEdaXKLJ1BF16rwmoGy5kGg8vIZTBqXYXks8USYGjG+D8NSkQxJ8fcJemYjR8VqKdAjOVBqy2uL7TOeKAc1FYZaqvSz0kogonnUwBhtLnPGE0BCIllXPjPBAZUXsXhNGxM2MEAJWww21a3u0S0gl+yLjzyFKUpHtfqB4FN7hP07SXouQoOcp4F1jKbeeZrBlbyBdM5aWTnxdMC195DxNWzGmnhR8xnWJstAXQJQUq+SSgIQr7eA+5ACdVA82CxZLfp4HmDWn43YbkKwtAl3XmehI+LHNZ4c3otc/pKUWTzavTKUsINUnP4LtOVpgYJ211mIfgBku0j+bUl/euwfvl1JMGgPDOA5Ond9URGTkBmc7Z+K16ixF0Ls8LZIP8VgsDj+i+F/rabFOquz3yvCQmTZIUVuIaMNKuxBZOZsHmnBoe2Ukx7lFiBAqrCryN2IQWQEWkJoZ7TEUD9yIUUQDFHi/1QId6IhOc+amIAr9bJMvYFwnmVTHBQO8kgkz5TzKGY/WBKU7J0DoZ3h4MN1gWfp+CS4YwjEOjEQIEEGjPAJP6uOxM43TXw5MpwU0IcLih2LccO4DoOtgWb4sh+VzbzP6LK4baw79i6YRv4t6Sz+Wm4WHp07k18CIsl7ivZAZPA/av1QieMntHyD9Fs8bB5nx3T10NoU8+8nevEFl9h2qRR2YK/+4Cf+yy6UJqp1nR7PNkxkGw+4BvV48uYmHdlFhAY+QrNRiU2H3smcWUu3ZEO4JjbzX2ZYW9WIRYMoHCMAAi4BksLvaczW3jp+nc7rXscm5ouaN4+UDZixS0XknZI0NNQ0FPZ4LpTBKGLV5QPdZQFgAXzj82xUB8PvfLuPZ1fmqbBjRJI2gpMmdSYCOxhmG4FkShnZv8uorue6FvQUQXeC4AdC4WFANE3HC0Xt11ah2w0+r9IG34NXEYh+eNe2+N5ZRMplhOzTplgJL55EPw9ShVKz53yKgyeDtI5qTT2mczKKGQ8XvTEpgtH5kbN9zxYmXUoxDZZjqgo0XQ3OJnPASs+NMUCH2jxg07pKUoNk4KVLYhbgezEhdPu2TeMFzh0bOKIwwF6Nt04anMBsFXvKkudgJVdGjY5HhquuyQkVHjM7ToyQv0asrhEw2mFKdzz1y5YX8/zrbgvWvbgxrIDUub48ERhOjzqzvLkKqW6LEC4mE648lU5K0U4HckQkI0RHbkFw2lyfRjCsP5pis6GiVHwi0FOgalJR6A5PsM7zuN71p5jYVbCVuml8dgblUUF3rhE67ysS0nOS9RJ0B4oxVoSmlBM9ki2040Vxx8H6IB8zb52XCc4i3ss3FhsAzfX2AH2XucjutY913lMwBx2DuhUd2RX/MaIHmDRgn8GczV5967iu57oa/Nil5kFsxAui++idsiI5ZXLCS6sFaajOJKAuyzsbldrMkdgLPEKAFalUbCBjR0lcnEHnwMnB/JvGG1+bims1JoYyZiCi3HGbUhesXzvaQb9rxs893tQ0RQM10+yR4pDjHwPIAKLcVRcCoYTi4q73YcEGpuFc6nRRDwftzozed3j1C4gfnXXjuEVFy5xTzU9feMKSCDroSJbIE4sGII0HPiuoUHI/k5hbnHWCINmOOkC102G89xrVQ/H9pm1OfUwy66K/Q5ln8QisJWyNv09gCH0Vq2gj4eRw5CQ0CN0J1XtE8ah8/U9YhslZ1l/2yeT6Vc14Ieg/Wsd8iV2SY1xZYQUFUgVRGUXkeMOyw7QJAtU4h/16DotPczKCpUG0odOT+EdtX2rUjpBdU597nHQrnVWJdfR2+Zhkqf4MV6+Zx6ih+TMmLtJPkq+CG8rKwCDst/kdw722LUdmRabPBS4Z2l8N7Vhv79L/Tp/tGtlAPiZChtEovNlLqawqWTRqUeLfGO4hqJWXhW/ZdCkJH4hFtMaEQZfvRz18RluwYm2zqg9WBtWr6O7c87XIq5CRe1CDLlMAjngPtdwpoZYgwFIug7O3CkurJmmTsz63gyVE2h67QkND+TbsLegl/u9cxmJdFSjQHyvhUecEu2tn6dzotyYgCqBBC5/hZr0QvcTN3Xi5AdsF/JeadVxqP9rI22/X1zOzceC/9iY1LAMDmgWPoDvl+UZQheWtc1q0ITuqr1Gq1YhCKwuo0I7mla/8EwI6Yf/4axUHjJxzEd5+/73/tGIl5GQ2tze2wz0gss0ouwbbO3DfdANM8JDiFWBV4YK2l8dBobNBbckzTjKOEVdYXVt8hT1/x9l61GEkdY9OeZIMEDkcJrQxHmxTOtXkPEVzaj4C7TXzyzomDIfy6UW895Vmp6skwzuVIDr+reFKHJdpC0/iWhvoRD7T3aecqyO9H9L/SdtGW3PACjJneBw81GjVd781BDR7oh3cOy4YbzMCcA3QM97gbXgyWoHIxhSjVdcVkZ5F1OSkvV0hsbSK08nLnLMZ1jsErVhR6LY7ItAsLSPu6LT2siOgYWnD7gBLc86uHNdB/VWxAfB+yioVrHBQuPcYK4Dy0SwIK0jYVwEsppOVaCmlWd1mhNIugIIEremWKXL4xQMsEfS0IaQ9CXsF1RlPUcY1ru7QDImd3j8BqkVIALf++xv9xMd/wi3KEGgblBwY1tCobGRHoz0lMRmnDNugcgvZ9jYmM8e6iG0rLvuFEx51pG5gv3SckUWk51EGIBURwpZSp3kYQhcQxj0BKFbR6uWS2g5N6xwDoS7lT+nN4HjbaleIvTc17d7IaObhTLVBjBDTj2SGLjRRp7UTugiJhfQJaKkXc2pSMskNg69yq9ask5pQyI8cOeE5lKjCGxLcYhvVeZJTrpZv8j48+leDg0rlhXcxk9EEI/qxuLpcegDwOwXFsPJlGDc6EWTl5PTJKHZhP3zV7bzpA9mz1xU3TPaqmBKBXYmZiOiddj/KRnv/EIADGfnIc1MJumceMXqxtI7H6fVXi0IMMyLXEGqalg8Gvn3FyBkUcKmEB6nvQl5b/YuJXhJ6TbuSkM2HMzptvsRVdl71Xhm5kgEms9rMlJri1g1lPAdD6HhPcCv+eYi2VmaaClxiICvzJa0H7PmrU0+3euErQ8QUsOaZ3VOZCj955vOoQHU8bsHMmOjrLxTKdiyWZFrd/XlVv29UdCTfxsSQ8DzV+x3KdCAn1CnvVQLPoqtCu2XIU0x0hceTAQiuAeUhIpnDxmVteK6c3Rupn9rcoe56E7bc7WGR2mpNjqobYuMG04KkAq9+a9jQYjcJ97EMixB98jFWjMA5Wox3jUj8tsNIx8TgHkuRl8n8pPrjiW0/FIRAana51MzOtmLNgcEl2O6IEQ+rmZJZgr0iE32SkymAcpDIEUjhQmUaFHa4ef+YLlcYiaAsWtzE1hBDbRaosMR51FvjVk6OYXriePw6OVVbDgvvN+Qe4V0PKoKWXsR7/sEA3LahpbTU2rgjCeR0yccwYMmyK+K6OggNjcK5UBK2D9Pv00G3FFy4ENEGfFLpmhEFg2PHimhVmpeKm8yAOw507nBkfUbAjANw8traKsWRGpIph8naPRmm/QgAQOo8CPOZ1NOBxe5zKVcyOK5pa+G7E5py2hrGirraMREGcRVIyXQrKkIfZJQyDxlDBpJvAnUTtTQHwcbHvB9ZlsnDVTiJ7pwJvguuae6GFsIdJuo76lXF9jYDWobh765SdXTfuSXMHPNKGUQ/Hi6roQ8loYfF4EHe6hCTJ7rqQs0+BaSsZNzM/Wixsdxg2PeSpGyNF+Zb+dY4+JBtCQebNJjy68ehZqHYDt7ZJt6PzLw4Qo1KsRE559pAMXYxiOEDzIefqRcYIyeSfe9bFWKpLJKEyKO5YZMaOwlwVD/w7xG8ShD5UasH/cGWbOa2Js1ZsSCetEJ0C3kj3LC5PUzQId/1W34gbIBbZhAuMMLD6xervInjGc3+mCm8U9FY5KrHG8L+ef1jzx5+gV4+X5kXkhiPS6cFU9RkGrq4kEFBfChfAAA1EAxH8IxcDKTBfih1uIQ27YobAKLga8zGLi39xTgz1vup15/uxp1LcKmfjdclIalVtk7SiaC1tSBEJdIVavQTqiUKdeP/kZD5mfT4MDCZktNm8BR016Ib7QgMwoCmvSBeacVaohzCgI3WKfDhgywqiEG1v8uqJeTpHWrL+7LFZAKbMvZWTVIV5EWMNRrNrZjw8NjwOlxYGmAI00y03OE5WgujcS3nXPOaTSBXw9aIH7Z3WeagVxGoDjfIQXeUiYEYTcWs4nW3swlkj+1VoBTD6QXIOAuVoqPmaYxdjqWIDMFKLxAlujaAl/Cd33Qt8CHQihEF3+yudhNXeYKzUhe1eXbIF2IPPmASURbCIMtMvUtXbu2T9u+e9Kzm6c0EOYgJrXF8sOWSmHKnuAeSlwBfPve3EzpzM/SLlYwgAFpO9Ezzqg1VUtBF57nDFxjH+HRSo4UljpNdT++EBRsP5z7SdCitiED5XXAmZ9LDuN68KS5x4hN0r+G3PqVqlQYUdMw7NSNpKnQgGh7Pgz4K08OE+07F1Z1ntlUDxPbgrFSIuOsIqmQrCg5hG0VhToEGx3ARJC1N+Xii08nOK51PoGGwOGIG4dh3RxI6YIQnouLoRjfEcGAo0G8zyTF2M+GzJe5nPbJ2TFKOCpxuOcBkxY3onB4sMjDmdMuX8ISUZsxa3uaIfRE+6Y9tnaAq5gK3YOYMyqkXIsopb3K8+h1xJC+mivsP6CXjd87bQJWEeTxhrCQLLYZLHO6yB90BGEl2zPwD1LeHqgwtfQcS2O6b4X+pHOR6alZqXVVDeRB8hC6C15bRVAPLs2AkDeAXLZuSUqCmYfkMEiJ1xyIY+zRcgwESje5tjYQ5z53nEiDq3jiYEzt8xqEOsI91YBUJinegHE+44zVyDqKadF0WnZGOElSMArPOBDGqIEnkpU+bca6PMh0pXtLnxaUUIMUrHN9TFueuz5qMdQ+lajujTegxZa3RBUxiU2UOM9VSCz0VctimlelIeyJzOQ7uMTHoyumFqPtsbT7UwECGVM6JGb8Qh6YkEf31mbKcdBWPn6MM1RJ8Fy49gVvUiRXXYkIGQB2lT+2BFny0bwuGFIHAgBrwU25RxRKfTcD0MlMo0jt5A7cm54/KcswPZMogK2bwDxQ2MY1F9ohQd8au+43ACW01Fhh2Kk4iAkwmAqBOgasqF6nb0Iz81Z7vk6lwsNuJbjoFcbhqYUozFgVC5OJnSwM6x5b5otSnoZb1H4tOzDwCzxOtZjPNgVuXK0SafSnMyZtQY6mIUSwc8l837pRnXXlhQ4cYYmGYV937che6Lg5vi4NxtkCv5qFTeMG44WRkb3XfgtLhWcKRf20OHniuirExYABYufusQ0Jt1IViX6OLa37bo+AbowAGbjXnYISy8aSM0ytualsiuZJu0gaGf2HJ4MVjNB4AHNqvRo3VGpNJU4tYm5yDXIx3VuB1jTvIYhLTQyoZBrW/lBNIVP5aeALjx9EyoR5KcrXds3RLyBhoMATRRNvFGYW9l9l623Q8hXwwS2TsPZys3TDQufT+cXO3BGlojmemSB2Gjt8x3beSr5IU7Sc1wQm1/m2ncQPkJY3IDHjggX+fg7kK3MXZlS4dWAJ4DEuY+8RlkALQaYnQusyU9hXNR1lmEes0urfZ9QS5xRO0u0xIiKW8ChQgkEQdR4mckDolZoSwWNZeT18GA6sNzIjV4Po6EBcdwOfjDm1JsLIrsNWMwGsb94zjW9tEAXaPzSO5qsvqPfQbLf90IfYtkOLTaKDD1tlhLITItJ4l8eKBCujyDy2uNMzGOhMCgAX1QKnbgQ4QJHVP4yDwTERVNhVNdMDv6VTaZVMkjKgKhuqdTyi9Et0K+VEvQZWvx2oO3cAmFqoFckcr7SipcUVBwnBam/Hvz8zYqVM42VQiS2OudhopIz76Y7xMNq3WE+SlaNUNhOGNpXVOgkDljfY1SwJbe6sZhH8jsBsfEUJ9+I8w2kB1FiJDxqEcBFSAV5HbPGqgLimmSjvRR+hC1FS7rgdOQRzhiKmeKAG5+6Rl7lhLlEIFYeWV7e9oNCbL5pgtl6WyUs1bQoU3orhbqnn4YQWkpAn8p3b8prOFzH390Ms1K8CHgHW/8uD0+hgq7Ws8Kr4NOoaDOAcoAND3hve4k9bieAaQhN7rGahtkJkThMjMIvnRALBXaZmxibK/HOdM0GHB4rQlzhjdryvqH8XY60ffGSaLjOpdNs1EdoPFNcFrFxnL3XA2zpMygXRwVu1Csjy0upHzwgFIaagsQ3e+ve0vY0s26WGxrCZehR7c8Nt6ploCvaqra0gkLQH407YI5tCnQWlYTVv4XHGwBd0uJk/jtLuVkeDvjf67GNhbgpIoDo1guQHpA0dyF5OteCyBeOTpuhTMq9O4bWEkN6pc/DmEWUcA4XJPrmAEOAnhuj5txr00EQiM+JbhCnfQnXxYVO9IKBb9DZrKeAsFzocXNvnndB75Z1NOlCVlnzXmYxm6BvraOJQkUie6cKGQDj4TIxL6lMMlPJnqfe5ni6jcgh70wFJj+SDdzSb9XS9/8Iz3DcsRaHMhYXMG0/tp5gVk1Yp+xaKwivMy/GAKVFc7wSh2G8JKxo4tEqSNfJDRKvOK5Qdxz6QgWmZU6BAXqKMzCOYmEA/NS9PCiJ+4sxOqZHHse5DPLL9WRfqBC2UoR4GG0SRmANkIfBwXH59+DPsDz85FGJPvw5n0yeqCfHBeRMI23vMPUVdP8LfSCLVQ4AwPSmPL8VotGnZWBKF1y6USwo524qha8ktMGeOM5oLPWufe/D4iAjCC241LqWkuWZEiwSkZLRQUvLu/zFUPmcTfFaeum8SEakE+NKYzgk2zfG5AIx3HbAG1OlwCH0wcwcgIHhajEZky7FLSVMxvbHnIsINBJGcIGwIDe6fT+7bYb13VNg5W6zTTpkYtEi77k22ObfbV49fU/yXmFJEbLhPB1S4POZ5BsOZYCPCD05tHMMr/Stopcj/jIAm02+CPuwi6ZBSuXwb8IQ5VD0YQw+Pgb1GVdQlVQAZb7o5ofAqR6Lwyk14Fix+CjcO4oL0INgP6Bo/as5xr5VW5sOgxSPYNjqJddzp+WoW2wIRuLtNDJcA7LFSoVv2SCvQkK1W+sxHCkzzF3y+hnA7hmeO5Cyws/Zpjdq99dQJvVwmJqpBCC/X+VxyCjkOhTFTkMWGFNsjxVTZjWx9xaupJcl9EXkIwCege3tWVXfKSKvA/ADAN4OO0Tlj/rB6BCRbwHwDX79n1LVf/TiHuRBr6IF2Q6W5dUAMqVzStwuXGS6uV7EUC1qKpN4XAn8AEgXjM+ZHFY/WM48s4ICh3PGocBON9A+0FZcb+JzCzB15ElN/pzaJ4XMHPCNSUlEEVlLI0O5+EhLo2b6tPMwaCN4GFWxRbmw0rL2FbF3xqiIJK0QfpmbNXrluMCLymqf+wEv93k+xoQbXDgJLK+6jDXSDtn+VwUCDYFVhdtECKWNY08oKzOuqiWV5+GOKZto1thDmgYsEY3XmMpKRbrTIlA5n97nxoUseysBJgBRznUQFEHSgNZK6iiQ8E7D4PHYvCZ8o+y3M+c7DimHU1rbzI6pze3q2s43Xeh47Uys336sn2GleEB3PYV9eJvFE2i3C59QyXC8ags/9EuCtdKowfLI5qEhAkQcIFoZT+4hLKlwIMZ7NA4TrqUXSH7V4Ol2yEAys20myhDy0974eFCoPrZopV3iaHbesH0n3qso5GqkUJZ1wli0gi+hV8LS/z+o6ifL7+8B8E9U9TtF5D3++58VkS8E8LUAvgh2XOI/FpHf8WJOz5puO2OV9wgXNrQhf2YwrQRK55yMCFCdjwGtwYoWd+XmtJA6mYtu9katoKMe4u3Mf1y40kuvndoaItKvuPDFReOmaDXf1v8+eXphHFQiIf9HgeaMEvnSkOjTXTFDwJmUzwoLDJFFtJyka1wP3Ej4BgN2G/NZ5gxApNRVOKzmjkf21eIniCEZHZvsV9O6Z7AUa22o3Obfi8AfWjhU694FPOcwvLhNXrLcyKKy1kzQTv5Sjdi+K6DwjCgT/ExfBvIMksh36yX/POaUfOZVYDEHYRLbfpiaZRJNovZzNVT8GUNeeCy4Kfpery8CbIjrSM5ZWP7FuEGDnTfgSQWEiBijiKZ2G8lAJDPBpKyFj0MbLnjlugHmE8Vg3robpILxfF4Oa3HPAIBEnC/HbMwAh0tSafUNIBvJa2jE1SJKeuXhwWkUXcV7b8r3C3QEn4N2GHtfRfzCjTDAXnG6nWcf1HTxSqGkHUFod5CqdwPeeReAL/efvwfA/wfAn/W/f7+qngP4FRH5MIAvBfDP73g3AQ6P2QLXfNnjYCkAx+VtYaYzSchkNzY0wtGEZUpk+ZtmSlZAFuqC+ByGDxLacXdKg3nyIHDA9kIXlotrlLvzOTgAwj+08szJ3bqKm24Qrm7MCfk5LAEXRj7W7blPpCZTV6FPj6M2XGPsop5eVXOFSTU9sWYAhbDncXx8rgurGkALgXOMxXJ9qzXobrfA17gh6jGWUz9Y+iCXCjqeE1xTC6GIVOBIN4xBIOaMhVyHlpAKi7Oap232jZriFAyBZLbHrpua11RvhHi/FoEU0EbPSmRT9mnlb1qPitzmjdlkbiMUMOX72MQXRlfJFFuFVYIKeQyjwpzyPIeAKmi1u1fLoHWRy7mmnFcgmyOWfcw1oBFgF3LMEjeNcyaqocZnEo6SmvGS71TjIjEX3Fu+j+tBTeRPdnilbFh2Mh6vuFzCd9wXm9F74rxVqsH9VowAbQnTVpgtkgU6ldD4navo5Qp9BfAjYqDm/0NV3wvgTTwYXVWfEJE3+rVvAfBj5bsf9b9dIBF5N4B3A8Dm8deiHawD4ME7PIoHhyK4Vxa5Fo0Yvg6AAUlaJ74ocdwaU9n4WTAyApuPEvrKuB0mwH0mQnghN3aPYJGGxl9ONC35vVvTvrG08aATWIdQv3dbEn/OZxSBUfrvQ0dLLdxrr+aM+AIKI1MoF8ujzsdxHvaQFVIxe/Z6qZ7TBlE5ycwJKmGenBV4MxWClHkDgNny/adz7/SJ8oreOrfGA8L1H4LJR+/gf68pdhX/je/6/ZZTs6QnyTz9TeuB7+dcZO1E9PEpgoa58HHYRWD/ADYlpZNz7JZ13yHSKQnvsE5gklozQHhH4p0B8mBCnXZOhTgsapAYxygdkHLSGZMBqFwjM2wuvEfvhoKXAp1z6RZwpNMyQEsDTtKTszFLxpAarJuHJjxCgyKgy+JRWgtwCaVKuIZxCID8BbuxZBLF4HkUT50eBQO5kX1TJHEGqhHrbnMpacUzC6cdKZWlBO/F5RsPgQHAVO04crIq5mWspxiUwxG9XKH/Zar6MRfsPyoiP3+Hay8bxrHTaX805fFeALjx5rcpc6nlKM0rOkpyYufUtFF9qqUHOQzWqUKgb2BWdk/hUNM7ifOCm39S9FsYrOQ45KHjgnCcGJw7kahGrcFi+AJp6emuxeLgrPUpNw3dP5aWR3VxtWAocItHlOcP5Bh1Mlc6MPkSv6CVGWPyv4cL7O9KXL5mJNTAYQS/eoHUfB50AuZbRZihzCGZGjb2fuJFOL4WDCCb0DzKLOGmc2EZQWCM946AcPl7hX4I1XCt4Zb+pvWwsKfWIU0datYhm6KrDoZBzarpO1/TxZfLY0sUiJGexznkBud4XPm0AjM15/W0qnWMezU1IeXv2mbX0a0IwZ3G/LWCaVNYs44AQqgn4xncMzWLywaXArFa/9EmwQX6eDymop/4fYu3IAsgnmEkah4cK/CZ/BDjp3GjiAaL2cbEBkpFEN6vG2fa1OIxMNkyVVj2JL+fQWgM6ZgxJkWcKDb06QnLP+fJEIKigJi5RS87+DhPvwshLyP0ehW9LKGvqh/zfz8hIj8Eg2s+LiJvdiv/zQA+4Zd/FMDbytffCuBjL/gMWstF4Gd3ynw3IiZxzR5p6ZRThLI3jKTFITAIqB5G4szCtMDJ092ayNB+oGLVFSO3P9o/fWsbMbBct5IGjI9KQy1+kDg84kBtbixW8AJkbKRAkVQ45tbn6k/7jCcMri3fweeZc1axV8Iy9j4Iz4XVg0PaGJCQkdo7R01BpD6Wzd8NLzWPRBP/dMuMmHt8j0HB5tARhT9JxrllZWPt0sp5YT9/UpsBLcqBG4pppeLY+dQ6JrFdPLVusAoDzfR6yhxS2PC+bUFUo1Z+ke6N9Or5rhSm1YNqCe1sXGJMomitg7UpphQl56NlawYUvo2OtEyPpKdb4bYSSA1vDDzko8w7BU8R7vGdEiOiVbqcSEBubCU+P4KIgahoFgrG3kzhuriB1PZAuy0OgTEvH6GsggdoTBEKFl7vitOrasmn5pWnMRkeV2mHHXtKyxwxi4lHopa5GIwjJlw0RIom+TO8+g5ISVOtTfkCPmplzl25XEUvWeiLyCMAmqo+4z//IQD/DYD3Afh6AN/p//59/8r7AHyfiHwXLJD7DgD/4sU9DBesxGhDrHa2qbnWaodTnEn0DNfJGmgx73Wa6c7mIrPoq+/8fFEAOGjEBZg/Wy3Geh9qc1reNYjZlnTfBQgMPyLxNcDrjDYG/fzv3gwspqTATH2rocRM+GopWpLYbMsO6F6bUA9iCBjH4SoyZkBTPnBu7mkvEJ8bAJld4psrFKFDYhGUJMzkxTPRwsGvt4NFRoYe4CbfTFUgaCueG5WKIION4Dxi8AbUcQfCDrEZJdv5BsxDi2oBZDKBvxGz9JsqJtmgtY7DBpkuSG8pBG5udo4xFCphjp4/cx4CyiiWGzuXbqYFU+vYTkt4HJPDO2xnMGwjV8A1NrbsAL1ZgqGaY6y8VtdPFEPb50hwoGXqPEgGldkrTut9GDcijzUAp8mT09nYKhgoArLuMY6PVbd+r8kPW6JVHqdvUegjjTABMmHC3305vfh+TFWmd1L5mnt5AYa9GNBdibOFR+33nfbwo1rHGpNpL6nogYCl8nk5OVH/w/jlXbL03wTgh8TMvw2A71PV/7eI/ASAHxSRbwDwrwH8xwCgqh8UkR8E8HMwsOQbX0zmTggkjFZUxeg3z8ugacmcJnizWtBjsZADsDkfMXxtNnEbX8SAGJyR6QUQygDss7aX6FzJjoHVW2AWjQ/Ffi/WXTskc1NwsodLhUEigAaMB310pqB6n5yOoRTdrrfTuoSbyQVLWAccjyDvFRtKIi4CALoFZmbNuCUeh1LQpWWwrVg1tPr5oHjn6iG5VX0c6OV/EY+YBewmG6mj5V2qomCrBW0ueBRR8EXhE/MqMbz4t56/rBPCup5ax67NmHUKS7/7UYN9h4gtUYnX+w4ZWUBYocuJZvCz5/pKsbBtTYz3p6bYtsUsfQGW1tBaBybF5jmJ82J7WL2ObxchMp0Bwwl0VLTMEOmch7FHETODwmukZym2vwirVKGe3mcKRO4V8pxMqZREawv18bAQ4tgkmz9em4YUFO6p86WTl7TB2oH7+9g+0xDkQ4tyGhBzkR2FZ0zRZ7r1gN9LWfwyv0qe9/E3nne7FIXh8YoN06SdD5N/S/fVkpU2nR/xWKGXLPRV9ZcB/K5L/v4pAF9xxXe+A8B3fFbPEW4if2lf8JqnGm6cW04ywwo2zjAsWKRgEm+t2Kfv7dqrJXLhaYnOkkEmriFhmcVKn2vTM4BCzzcA0xILfFHLuAGkpczrXEDlUY3OoFxT5VDGPjTHeGPgWEDAGcNzWs5pVF5WLwDJwExNCze44M/EZgOS8cK3elRjm5FH29nQDbPnRiuWY7Rc7nnUHy1vVlZH6tzRfPM6IBtfRUCbEFk/gr3cqxuykIoCmiazrHdtwW5a0LriZJqwaT3dccaRJq+w3Y9KLoLYs907WkQv9vwa8xniK2689I1COI5pwc7aWWLWht1mAfxYQfHGfdj7Oji2LwsCBlGHHecbiAZrnLdjGG/al4JI8uLia8kUZud/NipDqVCuB6JUo2I68372xcPoO+NTO7C+7icxowKIWIIsyMyVZvMf3l9PaJbeFfbAJKVFufNtdN3s6SEfezzcJ7VKlrUzAQW28V1IAQtVD6Unb5AneD4wFGYgNuf1kp2T+5pr69/fZ3LLVXQ3UjZfcYqzQykk3KKv+B4tuL4FsEW4UJvnAFHFsjPrNzNj7Dta7jW0kXXMks8D6t+RmrZE9lntZG0XkFZJz4MzIkuH7qkvYFbqjXn/jYe2Q0JQ1YOV2V42GNStap0SriJzMnA6pH8CowW55GsP/2oquL718dJKpgdEr6N6Il75bD/nxgkIh3MrOde2Pg61EfZy623YIEyfdWiOG5sxDga2IotigQWUC8xXg7sAzHubMZx9G+0gAGw2Hbu24HRzwK7N2EjDvm+w3SxgZoWoxrsFHIJUrsRpmY0xVJrDvEbdqDX7mkclaApQsGwUu2nB6XTA6WS5mV0F29YhU7fEgBNAi2easIsVTvG0qDiub5bCXy4JfR4jP59QlCuo5SQPBpluy1CpHng3vSQmSmyyKZh1zBTEmQiT8SALwmiEsZ6FVnnw7OKtDoqHNN/0g4ioQE6QVn/xXuD7ixlA4OFG6kcqco8TNeC4zlL4V29RZofVJvInInEk0pmB9FBEB8VREycouFn3ExlMdT1j343jqdlHl9H9L/Rdk4XFx4kBgiHhwoid8Bj8PdwC9o/lIQ2R7SHj/SmQ+hZRURvuVSuHdEgyHRm5nuwT2LC6tdgktPDiljp2ZjXFkYgCOxh6y/SybCHMLpv0HKZzi19U70O6REMxIBkVDvNoCcYGZEX3MizTMpeXWCg6YejxL7Ok9eSVs8TYLUtCcnz+HlHTAAzHG9auqelRwXrmSI6vS2aMhDfhY5bFBA6FQwTZ3AOjSx2VybTIxASsKOJ8V+bDU2BbSp9gWoClKXabGTc2Bzwy7XEyzTj0CbM2nG5m6KRohxZ8GvPl8x/8B5uPxQP8GeQUgx2oqBk4ZisOpGCYNgtubEzg39rssaiga8ONzQHTtg/7Jbquqgs/31OzC9KhP84WFvvwuQsvjpku+yPlLQK40TPfLNXvC4DLcstbWruMWQWfudHE9MNaWxB9moh5IwVjVN76+rVFIGcjbwKZJl2FJrNsuB9pkNVr+A6RjVaMivjcs8tkFjtdr49jns4B9VbqEQuQ5Nllk1l47LfFYsMalK1GRCgxzbkIg/gOdP8L/QIzxIHBBaaJQGDZ1NR40rOAZ9CgWoIiHhwSumdhIRYXdnbmdsssgnOuACyjZHRvq6sH0Sjv10nDAm8biROliFEOlmdYvfYzM48AG9N0lh1HabkP5+XOMBioFQZ0QRauLoryCiHlQrlAJuGhEF+nlU9rEBiavIXV4RtoOgOYkrecmLCL/kfId6ytAQT53B4ZQhL35tgr3FfL7esh1wz2iwf6W3eLTSTaCrMHEMcd/Fd+v7k74LHdbbxm+zxO2oyDTmjS8fTuFHracXhMRqVZopE0HqzCtEgTZ5al+u3Um5MEtBcKY6O4uZvx2O4Mr9nexq3pHAsattJxtmyw3c04kN89iB8w3pTvEy09Jjd+ff6bpqJC57xkd1X1+FDbA7unAJ1sDs3bygPBQ+mV18RsMF0kQbD7qhew0cuZH5EMmCLjVBEfoQciZU3nPJRGmr3L9sz7YBW+rbUqfbKmh0M7ifOR/42fOBALuEYmlnvGQwDf92I9tCg62FajM+I25GuNRZYl20qwvQyVW8TMCiKgjiIwbnW3ArnXQ24ZtEUwPzIeiUfsucITmdaEaDpGgbCcpMDRqaxBySygEMoyeQ3rWhzry2CMBmNzcbDTqJ6z79tn7WCdLe0gZ/jDCmNJ2aRzWhbHqaF81rSXsMzS2si5gVvh3Ozi7ud0G1hu2OdUbkXu+LWGL9tJXvnsaOTEiYMLV24IVzC95edACpbNbZvnzW0XIEdV0pE9c4KEsBzbbt6COjtX+qDmxG8jy4UbgRATA/e0frkp2XV09ldy5dEr/OT8IbDnvu7G8/gtNz6NN+8+g1M5YEHDU8sNTKJ46m2nODvc/S3VBHjDI8/i829+Em/ePYWbzYT+s8spNm3BL27egMWhCKWw4HyVvdJ5AtuZdRuFv+N8CujAw5qV4PSoVQYrm2tFq5SeLbPuamYXjRxmC9FIAjAGlYsXVAPI1dCjEuV6Mxffxu0GXZULJeCsk1443FyOPN6anZNB9izwsiwbRNfXGqfrNKRqNo8i+dD5LaBPLbGfDbB/DOnZ9CIbfI7qnongeHnGVXT/C33kBJrbmNhfWEtAbPRwdTbAMsmoJBZg48Go5VSzbSzghQ4pqaKX/YZMaCtODwBAZJEAPuGzxgLweWzJkBZyCrMswZd0B8UtCeKs5Z2H310pVHexMmrNxqgG5HKK2LhxDCXSzY7DTKh0tkANFNVMjXDjW2G4asW4gBWffwoOKgWD0DQEQ3g2pVDLPCMXDgcbt+zdMnK8lYF+nsMaY3HvD7D1a94srqbsabHgKq+oj5veH62tXZvx5t1n8I7db+A10/PYoWOPhv/t6Ufx+x77BSx3SpB+BWkrM944PYNH2xkmKJ7TLZ5cHsMzy6l13Wxcv9FQIkUiwaQe/7G/W8xmzA7jGRZRe8FUUho/LqDtvhpwbKTf8nmSQwgP14l992ltQxBwyBBnK7xu73WUhSf5GZ9JGM0elIH7qCR3gRznXkzj38PAA8KTr3MI2HcOJ8iGZ2Wuq4fOoHhdh2iO557W5vn0mKNtePHmY08XIT9Aki+A69//Ql9psWf7WTQZJpxuqSjdILfmRSOQx/JpZnGAZdHsdEfNrCmQoRKCpm99oTqlQT4fCsvl34hnFIwWSQZY0jNgkNesbA0sklh8Bpd8I+583YuVEe9Oq5QQFz0Zh19ksQJ7ur+EGgJbdatQOtBoabnlEG6sZ9xE9kEHGmxOa6l8XTf7YvldEAG88JpKtah0FyhxljFSiU+WdkpviJZMNI8TZMtbceiAG8QtzGVjXljg7IT73HqrAVcBLKitqRzaFnj2cIJfvv0GPLuc4qQdcNAJT8038ZHnPwc/+etvw+HAwdyBqnv6Eqi1js993dP4na/7GD735Cncms7QteGZ5RQfO38cc28l0GnZLlWQZQznohepbplWrJoZPYzhMI1weB2f41oBSwMkEiz4LLU1qTBEjRmxlqRmalVvjskJ46SgyAdEuw/pPqYwgrzeZSPx4sxqUiYq8HxtKj4mI5RsnlBiGy8Qc75jPQiAPIilZAgNwWS4KJkF6Jmua7EBu7Z252Uufu0cwDVZuC/K3riK7n+hD5uIcBc5+TVYwYkt1jW1/TEGeHgE6SrWQFLRpAHb2Fl4tvhzMvJQAVf+tWybxOlZqm3ZDcY4unFsd9Kwsokv1kXTYlEDGA9RITNSs+9SjkSOMlsgs7e/u5zTGcJyns6KYtq4BV7mYgh4H8EkbKq1nCKKYMKF5lhaCeyRyRnooqWm+b5aA39S0jyR14XQQq4XlUXMW4O1PjjkfNYNFcVdzJWuVmQRZoPL7L8/dX6KJ84ex0EnnLQZXQWfOdzEk7dv4ezTp9h8ZpMbmpuPBopYwHm+kcKW928HZGFgecVhHfwey0bx6dMDfuPmo+gQPDKdo6vg2eUEH7/9GPb7DTZnftavGyPDcX0lbpKBWvuvZsUxa4zjoOESUKenS2LOIQoyGGp8mK1IBsOguwHmEqiV2BRbP9MzBgDdZsovtLAm42zdhGxY6614hp5+ap6m5HMIkxTF072aefNMgVrogRRjob5HNUAITQK5nyK+JHl9QE7dzr/o24QfN2cOsbnADz70I1OVsTPyvI+/b70Q9VUh9F2T0gK+cPwgSdJNvNCrxLVyHBwMhAUZWTnuuimxSdfIwazFIqp4H/vbKwCRsZSdLV/JLFMXT1/LQCOQyiMPsfAPGJDzTYZeNgJd4pmMlIHRapFwY0UXwePiq+K6x5z7O/BZnc3uPA2NgiquRRhOoHXMc1GHa8ioBRJgb3MeZG+vbovELoI5nuSHEMaSczQI+UZeKNd6bnfbW7ZJWmya3SXVYwglVqFuWJztt3h2PsHJNOO8zWZhzyd47rCDHLKz5RAo92fwwJGNY0cc63KSBgV5O7Os6B3amO3gF8H5+QbPHk5wOs2RufPcvMNz8w7zPKGd2Kle6b5I4tUtz1rgmrRilVfDYTBCCEUWJRjrSkjGdV6vVeoY14axKit4pBGReEib3WB5HsiGcL7uBa6jJ0zDBkAWosGEbvd5pTEQir3wSGSYTYA43x17ElTO5gWgaGXkvi3yhYjBhSZoNFCZuOAKoB6gPp/mdUQbGIPgu2MjozJSwlVHR31eQve/0NcULoFVT4kPD1YJXTwK2yNLDwBapP6lMBdkxsFyI3temCsl4eYTo66QQ+0EGRvTN3e1iIcgbSuWh1Mqptzk4TaXfF7CKpE505O5qlICbCNPt41RGdDKwiCvGHVM3P6G9C7oCUGisZ02+METnoFSLXktSkNy00SOMr2uIuwiU0ESXhpgqilbO9dAGKnCU/RaYnNukh8CxlhsYBTGm+czoEwly8C5tnwv8lbfAvt5wvm8we3NFosK5j7hbNliv0zWMdMtTxKrLGlYEPOlsLNnaYk9+LuQ31hJ6vM6n3rMZ5lwNm9xtmzQpKNrw9mywfm8QT80bBx2Cwnt/L6cci6SB6r3VK3xyuPhedF67jne6uExKCpbCSFYFT7bEETFru/FoYLUx8KECOkWaGZgth7GzutrQVM1LGomHyp/TvndgIb57PJuF5rc+TumZ43hmMYGSZ4Ti5eEYUkvxb8rKIqAxpmUMzK6t5ShvGkpu8jjx40KeXZEjSkc0/0v9IHYMX1Sy6d2161m7TRqO1qEtDA9qMQeMPxbtXZZ3MQAayiKjkHjR/UsBVxx38IDn3gABFIo1k3lGFy1hCjwZQZ2z8sguIzxrJhlOVVUj0fOj84PvUS7RyYRGZ1M1UzYMkUvPJajjKQIFJWqVSqOwEhpNTrMRMEauGqMrXghnJ+ZgfX4ODdZuOGIlF3Zp6BhMVGMxXmBMFkGGctmgfPG1nYeYQX2pGHudFVioRQEWJaGfTeBiw0w94b9MuEwT6GwIutpQVipGBS8Kcy+BbCRrNgtFnSfCv+U1Nq+tfXpi+DQrTCsLcZc+77BoTfo0tIIasnrVv1q92N6JTaC7uf7BryzlxFLXiyYm8LnomLgGhFiDO/kIEOLZt0oulfZhoCq9/B1Y+yFSnQ5vdwLoWCeb5Z1J0xT4UGUvcu1DUWIGHz1XqXDOmMWTxIoAjX2fzEOyLs877fIgLDUYXukQkMm4K2LbKbz0jjUUAJhxRNq435SRFp4xASuoAdC6DPoF1kkLsBrlkpdUP6bqZGFQagovJVB31hgR0v2AoUNy9hZHVhLoZtj+1UwVQ+B36/Ut2oFK0XjR8CLQSQWX3TD9rjw6JavXS0DoFg1VHRHSqpHhhBCeIcl49fH4RnA2CIXxljt3E+pcve+WhGGnY45w/ZBWbvF2t+mhU+mHI8mjHUs44+UPVfoEeytbjn5osIphXe4ScaxZRl+KEu+d7M5wXZsnicz0LvgsFhB1n6ZMOuEQ5+wqLiwz547VTCCmC7smY0Ku1rJtAD5LA82Ro/2st7zYuPYLxOaT/ZhmTAvE8DjGRmf8r2BrhA/rYnBWumwGhQ1fos20wcA+0yJDqjMFX6eCy0h8YQttvcyrE9Ytu5dVKiFhZbsj0QFMKwHxu/XNa5JDLIgT1M7MtqiXgFpeGVNQXpe1YiIvUXvh9dVA4tz08cxiR+MFAbEAuhxF1N6taVQ0mIg1TvTQb602VK/FbZuhATjkCH3PC5A34Xue6Evmq4dA0ZVyPOamknTXEiwHUEIAUEURvF7ln3iG2DJIBSx4+5QxeEWBgyaz6qQAsTvvUXi+Yo86MHdY1r2AEpTLiQDkbloPWBk4Bh7zwXXVnubICxethsAjMl5NN+FoLErxOq2s4CK1qulnqk3hZKAIQYvZsr7VW+mVt4OPZBafj8s+cmUhG1mjbWQgt0yN3q+mcqNXiAFvVT+6LkRIgZSMHsA2Xo74gNWxEV3XRvQe8OiJnABoPvPy9KsqOhG+S4PyKEFKok5s4ZC/BCZ+ZG06NpsVdYbb23B73L8fQNgESxdsGjD7Aw+a8NCS5DGBwSM9FVBHwFMTYhyueHP3mdsIebEZWMcQsIEh24/c/2psDa3EcWCsd9oGAABtdAQi6paIA7WCUvXq7kHmGhO+K073/YdIqYFUStqUzcY3RGLOJzH/PrGjzlUDFkyhBtFAaXBOGQ2+DoKwgskpMi4l065P4FUHtaXiXMlAx/Syg9DC7k32Fcn5tLTTpdTYHFhUQPtV9F9L/S1mcCNdCnBYA0CPvG0VhXQ2ZssRa63uXsAEPnwYWVncLj2+5gfIcPDBIki2jVbBW4K7DxaTS4K1OaWrnhHQzLunEyFxRirTwC2sKh/U+8LJLlBYc/uULSeAa6KwUdKV7HGo9iL2QrFWxiUZ2G+2umwT4jGT/S0ptuuqJZUxISHeK8490AzOAfYOCOQqIgYzYAThwA4ggAc5+d3azva6pprsUQZC4lDd7wL5vHh7nw3s54yTREbjTFqFyy9oUPQ1X9WQe8N7SB2yPkR/9o9EPEDChwVoPmxm3xXruVyo8R2+qikGODrvWHuDZOY0F96w9LtxuGRLQCLfjIu4zCbpiFQ1z+ENfPbdeTt6klVPL+fZHB1JnShEt4G57VWrDP4y5JgFjJF0SGVzSJQpqkU66euXZtrI7kUphFXoufYsxfW0MSsND8Lj9LvX5MDCLmkMVUUqSvcvtNQiPSeotlgLa5yqJMesG7Zl6i0jeBn1VBC+T4KqoGi+K6g+17oMxMkLLeelnNNqQtYpeJkzvQbz/O/ICCLkIlcYs3FBUz718IKFko1FctogTFaPY0qhKoz9P6mhgUwtGmgpcDxd0D2HgyqC0zr9lyGDUdLdugKCWTP9J7vSQXG3cbrDXuXVKg+fmYkBUavZn3Q4ppvebGcW5bTWZ7hGkpKinVDb6lmkywI/DfeZ/gF5qkVhV4x2lpcc8GyiXy7nAceGs33rnBf8kRawzV7Jp6twNIFc29oUHQIFhUzpgkpUEE0Pbq/rcvEnjbFEKGFHEbBPufx0kBrRygdbYIOG1N3rJiN1Xj/xrUC4rhGfxIaIQc9mkcZz6GtnpM1MiyWPBUbkFAag9VUVmKKpEcWUHq0bYYFrblsTNZAKjw5pKVbeWs6Z9VxZr4AGLJphjbMkucHtBKYJoRUY1bh9Xs8jPzRDoJ22xXKhOFglck9Cx4KM5zL4O8SBxZR+PcC1ezT4BjgzpIlV9NuoSYzqiPCsVxG97/Qh7/8tgSH9ghLCUhhH2fNsrL1BKHNI39WS4EDLQmfbN2ka7nsrK1rY/DRMTPbMB70cmtFG2GGHCNgz6sxgHDPhPEBWqMpeGlRRMqgK4XlJBkl4QfExuIzI/Nhl4Gs2n6YbQ2YVRDCpPYkWdKFHzwrcQuYHotvCGE6HZUoFXI20I8U1bCmfP3YKC2VbCowE/auXPal/oGNwtQzcOrGaKlEaQmFRzXgsz5/PsfElmu8woqycg76TqFdoC5sZ7GD0VUFvVuMYL5JyYhUJHx39eyTTQodBj7BXuyaQojWXQ1my+KGnpqn0VUC3lEAqmKWffWeFFGTIDMM+qxGR8GZw8iC8TfTbnXSKEIi/BRr5AIzrPiiOHgIjxSPsAYhATuQnbEh9MygI382el4oa0Ql3EzoVi+k78pcuZAYkynEDnBpCt2W+IMC0rJ+gNASoT2uZ7RbmFj3o/735BV2x6zZfRnjKXtVC1RVBf0lxl07Q1ZZu7daa0JqHOJO1v4DIfR52Lc4cxDrqilKzOdeWArt1nYIreq+EUMn35UA6LKzhRwCogKDN8Qq3+zh5RnlQlrsEACLdWcM2Kliknu3XPyzECybXDwGjyPjBva+TPmzlLCstkyrUPKIOQ6XcYsCk9QgquXHi+dPK3CSmzgarVFBMrBKjHmr0BOOWWJeM3AqIcBqjjMVbZDPLQ9Xr5tAJ9iBNm592zym4hpOz+IGLx4Xn8eAarS/nR1228SUDrGR6g2YxV8gHf85IJUlU+xqDnuknnKTexqmiulVBs+jSKoqMcl7DR6NAr2L5+inIupdQpHFebjI7wAIL7J6krVRHo2l4NUDShBSh9hErLOm4LU/5j7oGzdMjlI9UQQ7jveIws7BdeobBSZECuwwN8PRhfksGjJtGbO37GhFE/416wiwNTgcHTpToVIGXmuiA2sT2mJJDwEzhhAeD1epKcs1QH6crRWBbsYg2Exusf5V9DJqwgPg9Sd3I5ArIm8D8LcAfC7snd+rqv+9iHw7gP8MwJN+6Z9T1R/273wLgG+ALdufUtV/9ILPUQsKzTfTyk08LDfYTJdskxVsAQfVTIXC+AMMQ3jEFcnmtrln/cSDirRoqiAormZATHOxBLi5XNtHy1jJ7wU+SMW0uNtZ0h0hEp0S4da6jdklvRZBSeuQz+bmc2IXUXSLe0znKYw7YHUM6thqLSohA1ZMnYLBexBFkBqc11SCceh7y2yUvjVLf/IYS+CutGrpChdIB/B1F96jeFMzEgO9DPKRnBMNfsnGa5B6HRklobIJgoMWGRXWtkEt00IsVwZFFLgzUye13FcROeYm8I0XGo6gh1avqespMQ7+DgDbpwtEcPSdGh+i4gkF3QF48VQ0WCu1Ahf4osxt8Dwt1Y7BYgWQmSxt/C9gyKpsfQ7bnAkQsfcUlqa9Nz7j+gUU180ybk2OnqXQU1dEFXbaHcU2+G6EXug50t7qNN4k9nj3Dro6ZXVtQKZ+3+ncPInwsJDvCowwa/xdEXUNQM7DcpK8xfvYM7xj6xX0ciz9GcCfUdWfEpFHAfykiPyof/bfqep/Wy8WkS8E8LUAvgh2Ru4/FpHf8WKOTKTW4mk2NbDJCY0Npt5D3q2MwdCZkJkIJUhipwuVwB0c3mEVqlsOU3FpoxHZNDIB4Hm/FOCzCcNlK+lhcDNQKR3EMgSK4pGOzLQpcQxaGX5uBvpUsnzIXJ6DHJXCVChFEZlVl422AH8HTzULOLjmlzO32fFqtnqwD3NNqtdSvRQ+f7B0vG8ImXs653vlc6cDoqcQr7HWtaXV9FIEPtfaoRS7ISIo2g7A9FxRMrQu+V1BuuDkMSqCLkXYSwh/8sx0jiyRL4rLvp+47dADHhcFIIU8tkdjoAXfLZDby1iW3rweRbCcYjirl22Jx/gJQlCnO1RgvvNSoFYEM+co6hc0PUIo8nyL0mjQvpQ8NZyrXP5uRzgWniZfSY6BMG4okAk43Cxy4jzHWD0QXRANFINPGywAXSDTVByp8CNv3+FHBSwgXmRMprd6WiW9M8ZqmsmG+aZdXFGKNheZUvkNQE1GOCZx3qZRhuZtzAv/HtNLFvqq+gSAJ/znZ0TkQwDecoevvAvA96vqOYBfEZEPA/hSAP/8hZ5FdxsAZEPBlAxVYYrAhp3JaxBHOsL9itLuSQGRaMNsFyJOqO9bt6SpvX0DtENZA1r5nHzNTSXdPAXm69Z0z4gncLNIBi0vBCeLW0mFMFRKLvk8qH3WPBc+gorA0GuIzBhWTMXk/TscTztICuStFZF0P4TZNriM7+/Ys4pYJo2vw+Y5n99TEy6EzmhNzdwodV4UebjNBMtu6sDmuXSPAW44xEYUBZqfnxweRK+w1MWA1xCABO+ncX/SBetaLXPnOHOqbwA9zTWX7u/O7CJfj8kP8OgbGXi49l5JuMzeXzXHoYCdHsUxTRZkDjjBq2ArxBieWi/GEL1G8pJj5jV+1lxQyd4mpe9MwR4IuzIm1PM7wa8Vq6YxQwjDlXRUeRePOYwvClYGR+ccj/rcLjc0Yn9VSQ0wXfFGwpPhOxdFCZhSqGne0fqByQCh1GiUesymKN2FMQTxmAizw6pVT0OVhux5xgATlh7bTzD2p2V/8BjRq+gVwfRF5O0AvhjAjwP4MgDfJCJ/HMD7Yd7Ap2EK4cfK1z6KK5SEiLwbwLsBYPPYa0OIMPIflZ9lcZjeWI8XC0YhLumBle5unqVrSghgBmOB1K48UYm9RTqtsQqf0GIqm4NjJjRUjzWzB9g1kU62xbi5j7wzJWNXptgDKO5rxet5AAWt2+nMBlbTKtWrBjkeOYaVeh5ADrU+/HG6EHsUAWm5c6wDnq25eSYFTtI6ipJ8pr5GANj/ndOys3cpgpqWn17iARYByfNn65iiBXNDpJKG5S2agjLwaQkcm1h6Clpi6javrOtgSihrD3rhSVO4bnHS+nPrcFDEfPyCEZsvH3ZFjEN9HKnkNIKZLN6RfdkLPh4aBxQmkR2iiI6WbE0d2UkeE6v9ozh+wJ4/b8o1SIFa92d0h+zMzZecIx9Ld/6sMBUTNqLLqntGlrHnio+3otd0iUUOPyUs8vIXVz7FmGSFdfA34VAKVje8hjbWW0Vfck+1AywJoMQQdFPWiTU8CzD1fNfaKdbmsEBHxXMPj9EVWk2iOKaXLfRF5BaAvwPgm1X1aRH5qwD+vA/jzwP4ywD+RE75QJeOTFXfC+C9AHD6lrcpN3GbxQJxSAHKLBHDmtVOwOK5ojVNq2DFKoiMiskrAVkJG2ejuhVC91En95go3IvFwlLteEPNewLOiGJu5/T8xe+Hm8nqWVrK+3SvzUspzCtuPfG5FFTdFQRkYC4KLIjh6LGhW1ro4ToTptpxA6jNiyiWHaIFdGRhiKcs8p3odXGe2dAtNpspzQhoa0JZQ0BaTDDVM0c3zwPLiR0bme59FnApp+fg3gkzhqh4SjyBcx/9+N3KD/6akmeYR44QrhlAVV/zvgGEUJq3d4j53yIPrFlKrxlFJAnU7LKaH18D8Mb39rfjcdj9JOeV/O9WNAV6NKCjMmesQGD4/sGqc5lyuNwAll0aFwx+14LC2m6AQp79pXiCVLQcQc4r90b0fzrknA3GAvmCnhj3ZOH7doB7HXm9uiFY07wBV8beAoT7Q2ZY2ikD52oGRsQXSstpewdNmNa94YgTNP+uGwLMAkSzLqnBV8V7Y5fQyPJyKInfC8ixQMlVlnCPsQXFVfSyhL6IbGEC/3tV9e8CgKp+vHz+1wH8A//1owDeVr7+VgAfezHPWU4JD9jEbp9F5MaqoqTVpYsaE7BFHDcHAAzs1kpWbW5NzkCrQR/JSQ24CEjhTmEr6YZFHjKQ1qILnWXn7Qjc+oke9gvQD76ZiyVPy3bepTIAikIgJEO37jSLW2i1AcnAPB7PuhsiJGSFiXofn2ObWxzGyYAyxxa912nhVouDcoiQi+a7hYdThE94MqUZXcWeIxeaAsqtMJ18nkuDPY5pOc2NwzjC5MKsnvXK+YpUVA/siaaBUPvME0rpRQlwziKWQ57wcceY51Qs4FC5Sf2aaNLmMBeVKNcTQLTUDUiH/zYMp0kFNXgDv1SwhMeCjzZWmTvfrPNfsH3CB2wcx/GQJ1yApiXtMMRx+w/nEe4T8gjhwoBZxI5XvBA45vuUxArG7OhJyZJB08o7AIANzEBkF1D3UKYDILf9PXcAtsacOpmwpuKR2XsmteRfknjngEARkEZF3yC8vBpf614RvZym992IRPh8xDtMAJo3dzxLXgfs/k3Hep5jejnZOwLgbwD4kKp+V/n7mx3vB4A/AuBn/ef3Afg+EfkuWCD3HQD+xYt5VrhajnHWyDuQDMuc4r7RCIBFsPQob7V73vuQgcMF7WZRRouEHbv7JeZuwirdMsCEFTr7hyfOThctyuiblevzYJea8cL35RGNtDI2tyVO+wm3j7AV3b8p3drolon0WHjsINs3B9zkc1cPZ+Dc0YIKoeCWkQmW0XoijDb5Qe01YyQOGfd34hF+EoFYSY+pQALHwWG7meHTAcP08RlVaZpbnmtP6CIC3YQGdpk7X6/lDxb0tk2vRfrUnwk1xTy59ZqB/hSeqWwTimMLhPBG/Z2XAqWE8Pc4Ap/fdbT4+zZPb7r0rAaksrvAf7Rsy0FAMc++ftb7396VfXMiBRO+fpJFaMo+MbN7EJL3qkHUaFMRMKifPxGeWBnvkh4Cq41pcZMi/bQjqqXr+bbRs6YYdwG1lkB7O5cBcgtPvvxR5lRsXHcc8WPwc0dkrS0neR3TMaMiejKeVGDI8utQYJsp6MsuvY7o338FvRxL/8sA/DEA/1JEPuB/+3MAvk5Efre/3kcA/OcAoKofFJEfBPBzsMyfb3wxmTtQL8BhMIcBHqQQ4c9h4bPnd09GrQGhyIIo2SVCV5qWX7HmyaBx0IP/ndWnNSeWG4XBqwpXZFqmlab3SbNGoOemt/8kNgFT9dCybXPta1I3bbjNjParfa+fFAsfeb0N2hiKvUsqPr/QDXWqllAElt2C6VuNIG8E1uXoedUqam6kLKlgmXedsQ3J9ynrVD2cIYOCwd+W96t8w1YXGaD2NZkFYCyEiuc8jwmEAn0v2L/e+e1CINceO2Yn+b0bwMriwGc53oNZsuSj5ZTQgwt8HqUHt+IWPzB8wPWLEgLCm4hAsFgFecyNPysSGWBzOp0D2Gcjump0hFBvI9xRBefh0QwyGpxIazst3mgdglQQVpyWHlCFK2zxkC0hOopX5MpyL+Ztz6lI2QVX/RZMNa7BcN2WRoBLgfyQe0ocBuLj6P0D5B2Oz8dY2ovU4rtqsExn2SM/oOs4BlY9uyeVIesSKMxr62T1d93MkmNwI+UqejnZO/8MKTYq/fAdvvMdAL7jJT2vIU0kzYUCkJtaERWAQAo/S98rwr12itRRyFE7LzdSq07nlnUypPZViwNFYBU3i3BQW8SaQtHKU3PBhoBVuRfvVxl4YqGKf5/vHZZ3EXpDb32FwSpF4DJgCXHLci54Je8pOdZ4v40dWRiZQurj6sBGAWUFb7HUpiUx5AEG2MKqbA/jWOVg98m0tYsCKmCDknUReC+QAp88w7mmkPGx9ElLei3TKVNAATZ2IdRHYVU/L3xEj6pCUhF0c+XRFodHOCduxVNAC6OWYHuPrFgOaEzFDIcj6z6GchQvMgNFI35D/o2KY7+OhYBxmFD14hQhcGM+3Bupp0XRIq5GUxZLFmXu68D2ywFRaK6Z9HK6llgLaN2alSv+wpb+al5N6xL7hMKwxpFCcQnGMSLXCshq9uzThFGoABEn4nySd0J5FAhyei75NPaqpAeOczfuJsGQWutz3Leac8/4RUkL7l5rAknZ9uC3Vp6z4yCzIIYzNz2ouNQDhgldbGEMxDbJJeod9z9yW/s0ll0D3t+b+Kr/py68OKaqZGqmhlmeflIQEPm5FFRkSgrG7JOdWSkMqnJTViVBDJHPXVyoV5hm2mdAa34kFRS/M9MaoZW7lyHIWg9vns7lwu/TGYaze6kEl5J3HNgkPRdRs756EbR1IxZlKGpKIgRqmTNagtVCzMZeaWGHRxawUBoQrUBlIWQJF6HwTMHw8z+/rz+fLjfjGAHhuBCIRAPh84p34a2P+w21g7zo7VB5uACPeeYrKSJ1lAVUrQO6kSJE7KE1xU84Xjg0dyYmxEuWlCwAyHNifA8PRrIocuKZ01QGvi+jjoLvwUEr7IS04kXIjFIkxUJMifVqEEvk8JeuQVZZxBVCNluMQDPXwyGiUHwt5zQyYFxwAiPUVxVECO3iNTAhpFrnhOdqEgXjNDS+jovRyB+N8gspE8wTMJ7lGQ3cH7VKXjcK3d0FS/+6idjcCBdoZq/ANSBPL6JydeHSt/Bq09TAIcCpFV3o614GQR5CYzImD0u5UB1XVNQ6xseD1Skol51CphT+kUnDSH3d5KCQpDmMsQMfA4dLMp1tGmtbgbwl+ikiG4d9OwgDbc4lMkziO1vEucNtL5xyqADT8xL36pNCbwikHNhiN8j/jJkxeh6QUDrsilk9GWkAqBAomPajYuVa8sD6GkSlMhUgeYKKuQh1pvySh+hyR9uHCnVcQdbKW1LwEwahMGdTO7ew41mKyGrp21r9bQLSvBqz0jPjw25aBf5AYgZGZv6ksM2jNVP5Vu9QulXAL5zTIgyZudMWAN78L/rkRBzA39/jTtx/bI8Qc+JrUoPZNDYiVrRRzDc0Aqd1Lwb2XdqpRLC5uUc1pcdNPrBW1unxcJ9Hpgx5TYBevIK2z95RMc0eM9o+q5ZRdoowGsLB3Ht2VEEgGBQ+ch5iHlSMX2pdDIU9BJGYImVvRFCdCulBtvRFEYdZA9SSLIs2wUbtWS32wZKfJYLAS2M650UrNFoklCCNMnPBmT3SJFGCydXqLBsrLDtaNuyciBwfsyBqEEoWz1hiAG8RPyVKgW7MIMuojJb6viENUkAOeGVtIkYFyLbSBxkEXVjafJ/FDnKWBdCDp/KRi7SsA9+x5Vi4kThnHBtQFGD57iBkyfBbmMs959+AXDtuegvKwg8jKQJIU9CRn5iVwoyVQTCVecyqb0T7hSFzRxH1HKxqjo1dlHi8EtlBi/cDm7Oos3CBV/uyA8B8y25QM3ciT79arpzuKmDd61k8tbFBLP6oyTvSMyBvnkqOL3i3JEswiB9eEq3TIdaWc8BrogMlbY3whgC4ocGsnDjgZQFEPaPskjmFMGsLDkGOnVrjuphfZDsHendinlLk7x/JFsa0+gbYPyYR7zg22GIPqMN1Dnf2rSnimJfwXvI7fD+ZR2VSPdSaWnwpbHUJ3fdCH4BDB5JZEP5iEdz1n2vqYWDFdPnPAW102xKyqGftdmLbbu3QOuHB1UFaJrrk6sriKY2SCxYFGJLMQ7wxWtxOzgAM/gmiYo/4nT0zF1s3CKYBUrDZL+PiywK0qpiQG1Jh74lTBEwWXkexUGvQfClCk03MKgY/1BIQ62V2js8ZrVDziMrcFKsxNlpAURp51DxUgkUzY864vyMLiwiZ0ao993Vyi3++odEIztJUJdNHp3yXKkiPjWsAeZCLju8da7TLZ0Sr7TnbAtcgI4Bcn6a2gMg2FaEwgCGDCDSGahbSUuaWHSjnFIY8HwId6MUjOsbAa2YKg+BCwUyvit7LAoBC1I2aEPD0Tvfj9zZniU+re530goGyP3yuw6Aoikq6QM6zp1Qo3mO4VfK9qHzqNEZG2O5obYjhzxavynoVAPAiPlfgUeUvnq59ml5ZFdCEACPDahmNgLa3bEJL2UQZJPdqrol4MdFdLc6626RuZYQm9ICFFpemLdbqlS2Do52rYjxTtVitNfBSA241yKSlWyfdT1FTCCEA/QDoEGD+/XYADrfS0mGmS/M+MhakKhvzUOAm197Rf4aCnsxZApJxmHmZrziwQxCcFe/nCqN7lsWQ6cMNHlkT6V2FRUymno6sGb4KvQPJd7ZrzARtBwu4tdl6G4Xy9qGGBVMsIF7Tt2wjkO/Ut4hYTUdRfA0W+AvYQcII0Mk2WngZszWZ43v0HSxFsCMhMMnrY66LlQ2VUEbmDRWMuwocKjS32inwiZVTEWzYibMEU2sWV/DpJWadeTvjWQjDvBYej/bgPkZWtbY9sH3WrpsfsTbjOmXcxFIvJYWgj2+hsmNv/H40985jKSgRrRgYH2NLhYZa/5IxOyqg4/YJWniPfEhjiBCPnZIlUTNCYyLiGjRyIuCa1lPwu3vOC1JuHEPPgduTt8u4GEsZquaLsI/5LSnUse95DySPimYxl/SjnliX0H0v9CGm9ZWCje5Yg2WL+MItp2lJkBqj4l5kw7a6c+T2+iPKAg3ueEf0AzFIwNuZHllBpGrpLKcIrFabeiMu2wgss6bF0HeGjS7FmmSqW82FFwaHuGkmC4QG8w9jKQKPFvOmbOrSApmCLrIr4rQmuyFTPdOV9OuaWJ69C5UodisbJnLPe86vNo/RNERQMs9O9SyTlorOPvf7u0KK+22O1r1Yy8Oh9JrzzbWqAUyU9sZMPQ1lUeA6+3JOdE2XrH3WDcoq87kU+LCuE4VAjXkUxQqkkKvKtFr6wBXCv/SwaXN+l2ONsTQMfW5041W4JynQ4v19Xnk/Fr9Feq7Pa/dD7ysEWten5soTgx8MLCDhn6o8ipFWPZDpYEYE+Zv7pm+yOjXmdlPiPhNSMcL5cVP2wJLeCuDJEAVmq5BRm72VCw020ajbGBfGBH50eoWal3VCxWzBdB4OH5XamutU30cnxMliSrlw4wI7BN3/Qp9a3S2o414bIbfK5APwIJCEGxpQULF2AZugKMNn4Mvhn8DWm42BvbPboUBNJQhcg33VVVtOGQTKcQQMwU1Y+t8Tw1xOFK1lUVBmFxzNj3sgSiHayt/9DAF6BMwKCGuvpULRSSDQFEDE8fmOvvkEadUeCzFmLozn8lpdQgQ6adF2xCEXdCziMHm3YJihUU/J4qblGjHdVIuiYNk/5z1wfSAOAKFCYG55plq6dGsGMWBX3y+t+7T07fK+AZp7Ic27lXKuA97Q8kzkmIECCfr6DwcF+dwwkB3PBcZxoAhD5DPoJbDojxlgMiP7+PtcMx5S+YtxJfH1Zn2IxceyZbhu/ChPH4NuNOI4kbUGDN5GnIF8FCOLvepB4YCNNC6Ley2eRDCdIYw1SK5XNBrka/mat32+v90r8+UZ+5IZdng8YLn9zJ7SvO+QETXDjTPv7zTEOjSUZ22oFvzrPNn2FQ70IxQbrM2H8yE9uvB6NwXiPnrfSve90NcGHB7vAwYWQs8nK35G+R153fGxdaMfVtVwulvRGKqUXsfXburg0g3jqbeiVU4LKVoklMwOvoOWoWjeqx7Dxo3PzRmvMNuHwz3jdTz3uyeDRbuKRcyyljpXgKiW749zTY8Hl2GGl/yJH9h4U4FyjBWnBeze5kVpWI7RbIoCs0W9isVcUNa3Fu5omZfyDkeJVyM8dWQ8xK2KdxWnZWlprSzmrfUOk77H1jwuWV89uoDEd5fxWukKuZEWKI6+Bh+L3ug4TBLreKwE0AVt1uhCe2F+evbjKTcesmeAIuiODRGkl1mV63Ja0mopzMpxjGFULLQAyA9aBGJ+Fnu5ZK5MZ2LtSC5ua/uxGjyXiIExgUATDit7ss7nhb71l1j1AIwvLzAexsGIDteH7FmOPgNiT+ikF9ZZBcD2eCBJ973Qv3HrHL/zf/dL93oYK60EANg02227tmDbFiwqmPuE5+cd2ij5rmUsuzDJgefnLTZv6ph7u8O3VnpY6Feu+Pt9L/SbKHbTgq6CdqwpV1rps6RXgo92bcZWOpqopTtKw26aP+v7vpyxNCj2fcK2LWhQnPcNbm3P0Yt5fr/vl3VP3xu674X+c8+d4id+4neMEMidIJojtzryjtnZji5QKXYCkO5XceHoml6IkR1BKNnRUAf3MQcyXn9xcON18VHBfqsRaWfDvoBbOXwmkSZ4oTPi8fcIj5QDVOp4zAU/crFxNO76euXaWosQdRdDgDTvVYbucRmxorYjiKHtJXr18HdCWMdrCeSziYECHOfRWI7n1d+v31qwuXXA6Y09Nq1jUcF+v8H50yeQ5zZDltMwZ1f8W6GfCzDM8Tg4hxzj55zj9OYem8mYY3/Y4Pz2Fvj07sJhLnxQnG9QYCxCLcFTXfK6+vxy/YX3K9+r8bUB/jnaB0NKaA0Y98vhwwtbvs6l2mE9hCp5lnTsxyN4Ju7h4x5gwEJXOm/HUG6kQuVn7OU1rPdxUZaO94p3vGSNhrEvLnOODl1/wXHjARD6sgCbZyVenqlggSc2jNyg+cI1ZY4BYDb2AmRgahZ9Lac5wd0xteatXXkCVWQDOCNHYG1zyUHL9ezdWkE7YzzqkEKltlrwIBsFFJlkOgdU5PLTcUIwZMZRFNkoPFBV0kHJXMgx2PtLCTaWvkWh4OQCPiqaWQlx/QLI3BCFOr421hraziJmSm4tia/Cjb1Q2rlkIH8QABJrGMr6qLVwXRfR0sXQM1ekewpiVeYd2bZjsrS4c6+GvPRcWufTEHRzZknJYmsbmSUlwBt52Ru9tJ8QeZi1E9IF8yM94wmcB49jtNkqpiOIWU5X6rsU/owNsKaln0jJf5fYExCNQGQINAZklzR4TKEiagTi1K+SenxMtREZz8NNg0suZi0xPlcNFjVlo3NJsOhAO7RhjzKYnifq5fhDQONoXUr2kI3hyNjx64WtATTXnm2iWQHPVOhYMsnPLB6ByGTqZd+jwYr+POmiTymDLNYxdgBFz3qOy+i+F/qACd3ohqgZpWYuMpmDGSAUXsxKYZtR5sgPqXBHVkloSM2DLoZNipFp2kEy/7jlpmZqJZlYNxY405O8P8A0P3Em9dxoESxbRLXucbFZtIvV3NDVajCBnt0S+xaRxTLzu87I4QHUVMxy/CI3XwhlIAXzRvOkpgbL0qCCCyVjE813YI8SCsehFoGbzjsk5qljeaJXnPsrSIVV0ku5jsp+72FlZvprZ0EdkAExZtfwFV0BLSzRr15S4c0qdNmvqApIZmwMfY18o+JQ+NWtRArPCBxzPArIIRUIbjpvSo6JY7H5klBWfQtLge25hjWbhYIwvKUZkXTABecZzlX5D2uysSI3AFZMBrvH5jnOAVJheBqpTpZNxMwc4we1fefBcY6ZjdT6RkIBRzfWEsiPVgliSi36R9XU36PDTpTdKcsaLifl8BdFdMyMtXYD7jiRIwy4linMTF2tdSeF0zztWHF4FCFXYuHpHcNkXDsHJpVIfUZLxdiWPNf7OH290gMh9Lkh2Rq3b1LYhzXJSXXrGMgFid4aQAr4jcbZn+I5wGxIFhkMnoMc53F6R0DZ57i4wFKEHK0KgX++zXNOQxD6M3lmbJzbWtvOisaRgrWqksVXYQWWVrgce7QZ6GkxMIUuXEQphTszokd9hRKq4tFmSmk5MaV2mYV9DPNwg/OdohW0YDjNrDf1YhfJteO4mBtNI9gt9ZrKOyiksIwl5xtlPGodG6tbPd2WYfNyfbMgpiiKy6ha3MuYy80c615SA+HrRmEXvesJB9CaLONn3jk8F304Mas+DmVOyolRkfbYk0drwRl5t6ZWalV6RaD1nWIG946NvxdIjfvsQqVr4VlZzLDSurbkC0EolajrKNRm2BnALRUlec26XEooPIAC0scwJ2+hATrDPNeSRkvlyhbVAOLQ9gFiK2slS+556dn5MmCskqocdSZlXrNvU+Gfuci4Ob+X7a89tXSGHRnrp4ctu0vcKqcHQuiTU6IfTFjmPJUekMmENlsyEEoIISzItgdwC51FQ5xw987A4gpF9LWvB2uHUC/zSve7NwBRzHR558HwTsSKOUJISI4PAGqXTZaD1znheb2jsHcLz93CXnBO+8xy8aGGebJJFHHzdu6CVHOzY2P35IHy07lVJVOI21qUDU1h0lzISwqh2tukCt0Kx8R7ar4fC+rgiotV133He8vg+R0rmzpHUFd4BZvSLaLwJ4RrQyjndrBeO2E0aPyY+fGTgmeu+m19PAgBHnGaVhQzFfolgjmEA/nRoZOl9Rz/JcQ55OH1FUfmfEHSYwrvt1qZMAs7mgJupTSEQ9SzIPabDH3vAc2OojGw5JNq1cbfuD9dKGfTNv8+awOQ85rN0PI9LlRBu3JQsbWHP4tV8FE9XqHho35NLKbiPgYweAls21INS+4hM0hlXE/kXE7nAvXi0Zp7DyDPNC57IOSgpmFDhEE6gNOLxgDp/hf6Loxi0/iiBZSS+3boCgnYoi3F6lf3BKK8mRYhNTsQzMUTraLiU/KepmU1mCqsDPakJ9M7XgwRdEkXP4qdCryy1A6VvvDaAGnG0MS+Ewu+eCRaWPji5f0ek5i882jgwuITqyXw6e/Hcu+hkrJ4DssO0NMyTxSi3eGdAnUliH4kVEAlY9d3t8iiPTI3K7z30bbMXTernAfaxEYXAN3OxI13pafisEQ9CcoKLAuM4qc7xdgWCfhFXXldCJ5zimIuJK7lu9KIoEfVFkD2OW+0AqNleIkjhccleT8o4oCS4z76lQ+ixgQOUx57DWLjpRLSjUbfm77VgElYfV6PnCS0M50BzRXVsgWwRcQksg1E4aMi3Hl2LK3b4MOWbQSiVoBGU/Hs6tpia7GKqugrXwLkfYm4AE/f6w3R2NAGMq5fTPCRrAAr5A/IszFQBHyHtQIvFn0oKzcYoj0EFXpATxJKJZs/ljHROKUBsVEsG4O+sir8PrL0ReQrAfz3MJ3+/1TV73yh70xnCOuwWtuhgbmpXFCHrPGJ6xvYwpaFqxDEEKhku9IKB4VJZ8+1VG0ZrAni6qx8jD7iZOQj2AQoAWFmfFSMswhbxiVi83cKIYB4OTeSfe6Wz5nE9Zmhku2Ma3zDvAxTCI2HocR85bGGhCrMUtP83SscY02iapFKBh6UlsAg2bo4Ni+qAs31m1w4x1F69OIId0l9B+RhKcxQKgdLhHFAWIl4ewNk0QvQxsL4TVk/rmvoJthc9EkxlSMRyQdDpotizDaa06LjWtkHfu+ezz72jq6iENLkZ3AekIKC8RRJ3p/2Ekon4mSuvKWXv4m3PSnSY7lRzoaOBnrZ84nrFvuMVe2aexow+AT+3s0hC22pWM1Sz/WnBJRuXk02lEuBWxMg7GjHIqQL73AxeT2NKgppBl7puVNI16M9a4O1MHb4Lq4gu+S8R4zGnx2Vzj3lWmRicU8G7Oj7b84xZOfT+8TSF5EJwF8B8AdhB6X/hIi8T1V/7k7fmx/Jn5Xuz7DjihbtiYED6X7G+ZLIYE/t1Fct9QEPV7duKwO0vHdtQlU3dlvSGu1N8sCHECAFBnDhUA+tIBwjC9BPgPPTS57hWKR9UDbVYt0e1TNN2P+GLZGnM+utEgcuCBWFDJZ44Iyh9CQgnuqzs23AMA89g119m9lIvD6YmM3qjk6sYsuEsKy40R0GG7K3gBBQecaqlvmyLqZD6wNfZzZGqwKMynw4zOYFrCebG8n+OdVjcYUqnW0UjjakUAlrURJa8F9fF0IPbjET3jnaCkDD0NaDcRETjH7IjrrXMY98yAwp/m6HdVh2TJ80vBamQPetxtxZ7CUFKp8br1mNJxo0VGY+732DzP7qGHBsxrOWLaJZGb/HNM1GHsGRoulpPFf4EUdKqbpC5HU2kaueS1tyHavSik6YUsYehl7yQ42BhbIpZ1y0xZWD5v3I19XrPYa1LD6YhtZldN2W/pcC+LCq/jIAiMj3A3gX7NzcS0m3wNkbbRXF+3/kh/4vN7mUvwsAbhZ+7laruZkCFbVgJK24aqF2QA4NKEf0iUrW/6s48IgUEFUZRZBzdNXi+X5O7rA4fJ/yHrJInFoUY7vs+vruXSC9WNiUEq1+jhCefavDWOywd/uv7xS67UU52t85JzX3H7B5TY+mvF+ZA8CFzaEeDEIh7vfk2tRXdmU5YhSa3hly8w1pndLHdSntsGMtONa6OamcF2sc13cKPDJju12wnRZsph499ZebM84+T+K92r50iNwo+kmZYwqPY8udc1TWIZZaktf43e12gYhif9hARCGi2OwWHF47Y37MBQjnsLR41t24Vwa+0rLGhb+Yt6+F52MdFomgfqwb36e0H4gYVbg5bG3OPZcxrOAFVzCNh44zTrVV6FHgWPYt9ntAHANcWN6nzmed+/Jcm7PiKfEcCo5XR0+mtgxR7qlybUJsZdE5RzzApbZL2eogL1hLMdRQ1DXmf0ew7zFdt9B/C4BfK79/FMC/c3yRiLwbwLv912f/9X/xf/6FaxjbS6HXA/jkvR7Ei6QHZawPyjiBdax3i9axvjL0Wy/743UL/cucjgs+s6q+F8B77/5wXh6JyPtV9Z33ehwvhh6UsT4o4wTWsd4tWsd6d+kFHIFXnD4K4G3l97cC+Ng1j2GllVZa6aGl6xb6PwHgHSLy20RkB+BrAbzvmsew0korrfTQ0rXCO6o6i8g3AfhHsMS671bVD17nGF5huu8hqEIPylgflHEC61jvFq1jvYskqhcg9ZVWWmmllV6ldN3wzkorrbTSSveQVqG/0korrfQQ0Sr070Ai8jYR+aci8iER+aCI/OlLrvlyEXlKRD7g/33bvRirj+UjIvIvfRzvv+RzEZH/QUQ+LCI/IyJfco/G+QVlvj4gIk+LyDcfXXPP5lVEvltEPiEiP1v+9joR+VER+UX/97VXfPcrReQXfI7fc4/G+pdE5Od9jX9IRF5zxXfvyC/XNNZvF5FfL+v81Vd8936Y1x8o4/yIiHzgiu9e67x+1qSq639X/AfgzQC+xH9+FMC/AvCFR9d8OYB/cK/H6mP5CIDX3+HzrwbwD2H1Er8HwI/fB2OeAPwGgN96v8wrgN8H4EsA/Gz5218E8B7/+T0A/sIV7/JLAD4fwA7ATx/zyzWN9Q8B2PjPf+Gysb4YfrmmsX47gP/qRfDIPZ/Xo8//MoBvux/m9bP9b7X070Cq+oSq/pT//AyAD8Gqih9UeheAv6VGPwbgNSLy5ns8pq8A8Euq+qv3eBxBqvq/AvjNoz+/C8D3+M/fA+A/vOSr0WZEVfcA2GbkrtFlY1XVH1FV9nL9MVg9zD2nK+b1xdB9Ma8kEREAfxTA376bY7hbtAr9F0ki8nYAXwzgxy/5+N8VkZ8WkX8oIl90vSMbSAH8iIj8pLeyOKbL2mDcayX2tbh689wv8woAb1LVJwAzBgC88ZJr7sf5/RMw7+4yeiF+uS76JoeivvsK2Ox+m9d/H8DHVfUXr/j8fpnXS2kV+i+CROQWgL8D4JtV9emjj38KBk38LgD/I4C/d83Dq/RlqvolAL4KwDeKyO87+vxFtcG4LvICva8B8D9f8vH9NK8vlu63+f1WADOA773ikhfil+ugvwrgtwP43QCegMEmx3RfzSuAr8Odrfz7YV6vpFXovwCJyBYm8L9XVf/u8eeq+rSqPus//zCArYi8/pqHybF8zP/9BIAfgrnFle63NhhfBeCnVPXjxx/cT/Pq9HFCYf7vJy655r6ZXxH5egB/GMB/og40H9OL4Je7Tqr6cVVdVLUD+OtXjOF+mtcNgP8IwA9cdc39MK93olXo34Ecu/sbAD6kqt91xTWf69dBRL4UNqefur5RxjgeEZFH+TMsmPezR5e9D8Af9yye3wPgKUIW94iutJjul3kt9D4AX+8/fz2Av3/JNfdFmxGxg4r+LICvUdXnr7jmxfDLXaejmNIfuWIM98W8Ov0BAD+vqh+97MP7ZV7vSPc6knw//wfg98LcyJ8B8AH/76sB/EkAf9Kv+SYAH4RlFPwYgH/vHo31830MP+3j+Vb/ex2rwA6x+SUA/xLAO+/h3N6ECfHHy9/ui3mFKaInABxgVuY3APgcAP8EwC/6v6/zaz8PwA+X7341LMvrl7gG92CsH4Zh4OTZv3Y81qv45R6M9X9yXvwZmCB/8/06r/73v0keLdfe03n9bP9b2zCstNJKKz1EtMI7K6200koPEa1Cf6WVVlrpIaJV6K+00korPUS0Cv2VVlpppYeIVqG/0korrfQQ0Sr0V1pppZUeIlqF/korrbTSQ0T/fy9azOVourXnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Fixing random state for reproducibility\n",
    "np.random.seed(19680801)\n",
    "\n",
    "dt = 0.0005\n",
    "t = np.arange(0.0, 20.0, dt)\n",
    "s1 = np.sin(2 * np.pi * 100 * t)\n",
    "s2 = 2 * np.sin(2 * np.pi * 400 * t)\n",
    "\n",
    "# create a transient \"chirp\"\n",
    "s2[t <= 10] = s2[12 <= t] = 0\n",
    "\n",
    "# add some noise into the mix\n",
    "nse = 0.01 * np.random.random(size=len(t))\n",
    "\n",
    "x = s1 + s2 + nse  # the signal\n",
    "NFFT = 1024  # the length of the windowing segments\n",
    "Fs = int(1.0 / dt)  # the sampling frequency\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=2)\n",
    "ax1.plot(t, x)\n",
    "Pxx, freqs, bins, im = ax2.specgram(x, NFFT=NFFT, Fs=Fs, noverlap=900)\n",
    "# The `specgram` method returns 4 objects. They are:\n",
    "# - Pxx: the periodogram\n",
    "# - freqs: the frequency vector\n",
    "# - bins: the centers of the time bins\n",
    "# - im: the .image.AxesImage instance representing the data in the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
